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45 changes: 45 additions & 0 deletions .gitignore
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*.pyc
MANIFEST
build/*
dist/*
*.cache/
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.coveragerc
htmlcov/*
*.aux
*.bbl
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*.idx
*.ilg
*.ind
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*.pdfsync
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*puf.csv
*tax_func_loop_inputs_large.pkl
*/OUTPUT/*
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*dask-worker-space*
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*/__pycache__/*
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examples/OG-ZAF_example_plots/*
examples/ogzaf_example_output.csv
examples/OG-ZAF-Example/*
31 changes: 31 additions & 0 deletions LICENSE.md
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This project is in the
public domain within the United States.

Additionally, we waive copyright and related rights in the work
worldwide through the CC0 1.0 Universal public domain dedication.

## CC0 1.0 Universal Summary

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### No Copyright

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under copyright law, including all related and neighboring rights, to the
extent allowed by law.

You can copy, modify, distribute and perform the work, even for commercial
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### Other Information

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62 changes: 61 additions & 1 deletion README.md
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[![Python 3.7+](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/downloads/release/python-377/)

# OG-ZAF
Overlapping-generations macroeconomic model for evaluating fiscal policy in South Africa
OG-ZAF is an overlapping-generations (OG) model that allows for dynamic general equilibrium analysis of fiscal policy for South Africa. OG-ZAF is built on the OG-Core framework. The model output includes changes in macroeconomic aggregates (GDP, investment, consumption), wages, interest rates, and the stream of tax revenues over time. Regularly updated documentation of the model theory--its output, and solution method--and the Python API is available at https://pslmodels.github.io/OG-Core and documentation of the specific South African calibration of the model will be available soon.


## Using and contributing to OG-ZAF

* Install the [Anaconda distribution](https://www.anaconda.com/distribution/) of Python
* Make sure the `conda` package manager is up-to-date: `conda update conda`.
* Make sure the Anaconda distribution of Python is up-to-date: `conda update anaconda`.
* Fork this repository and clone your fork of this repository to a directory on your computer.
* From the terminal (or Anaconda command prompt), navigate to the directory to which you cloned this repository and run `conda env create -f environment.yml`. The process of creating the `ogzaf-dev` conda environment should not take more than five minutes.
* Then, `conda activate ogzaf-dev`
* Then install by `pip install -e .`
* Navigate to `./examples`
* Run the model with an example reform from terminal/command prompt by typing `python run_og_zaf.py`
* You can adjust the `./examples/run_og_zaf.py` by modifying model parameters specified in the dictionary passed to the `p.update_specifications()` calls.
* Model outputs will be saved in the following files:
* `./examples/OG-ZAF_example_plots`
* This folder will contain a number of plots generated from OG-Core to help you visualize the output from your run
* `./examples/ogzaf_example_output.csv`
* This is a summary of the percentage changes in macro variables over the first ten years and in the steady-state.
* `./examples/OG-ZAF-Example/OUTPUT_BASELINE/model_params.pkl`
* Model parameters used in the baseline run
* See [`ogcore.execute.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/execute.py) for items in the dictionary object in this pickle file
* `./examples/OG-ZAF-Example/OUTPUT_BASELINE/SS/SS_vars.pkl`
* Outputs from the model steady state solution under the baseline policy
* See [`ogcore.SS.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/SS.py) for what is in the dictionary object in this pickle file
* `./examples/OG-ZAF-Example/OUTPUT_BASELINE/TPI/TPI_vars.pkl`
* Outputs from the model timepath solution under the baseline policy
* See [`ogcore.TPI.py`](https://github.com/PSLmodels/OG-Core/blob/master/ogcore/TPI.py) for what is in the dictionary object in this pickle file
* An analogous set of files in the `./examples/OUTPUT_REFORM` directory, which represent objects from the simulation of the reform policy

Note that, depending on your machine, a full model run (solving for the full time path equilibrium for the baseline and reform policies) can take more than two hours of compute time.

If you run into errors running the example script, please open a new issue in the OG-ZAF repo with a description of the issue and any relevant tracebacks you receive.

Once the package is installed, one can adjust parameters in the OG-Core `Specifications` object using the `Calibration` class as follows:

```
from ogcore.parameters import Specifications
from ogind.calibrate import Calibration
p = Specifications()
c = Calibration(p)
updated_params = c.get_dict()
p.update_specifications({'initial_debt_ratio': updated_params['initial_debt_ratio']})
```

## Disclaimer
The organization of this repository will be changing rapidly, but the `OG-ZAF/examples/run_og_zaf.py` script will be kept up to date to run with the master branch of this repo.

## Core Maintainers

The core maintainers of the OG-ZAF repository are:

* Marcelo LaFleur (GitHub handle: [@SeaCelo](https://github.com/SeaCelo)), Senior Economist, Department of Economic and Social Affairs (DESA), United Nations
* [Richard W. Evans](https://sites.google.com/site/rickecon/) (GitHub handle: [@rickecon](https://github.com/rickecon)), Senior Research Fellow and Director of Open Policy, Center for Growth and Opportunity at Utah State University; President, Open Research Group, Inc.

## Citing OG-ZAF

OG-ZAF (Version #.#.#)[Source code], https://github.com/EAPD-DRB/OG-ZAF
34 changes: 34 additions & 0 deletions environment.yml
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name: ogzaf-dev
channels:
- conda-forge
dependencies:
- python>=3.7.7, <=3.9.12 # This restriction can be removed as soon as these packages support Python 3.10
- mkl>=2020.2
- numpy<=1.21.2 # This restriction can be removed as soon as Numba supports NumPy 1.22
- psutil
- scipy>=1.5.0
- pandas>=1.2.5
- matplotlib
- dask>=2.30.0
- dask-core>=2.30.0
- distributed>=2.30.1
- paramtools>=0.15.0
- sphinx>=3.5.4
- sphinx-book-theme>=0.1.3
- pip
- pytest>=6.0
- pytest-pep8
- pytest-xdist
- pycodestyle
- pylint
- coverage
- requests
- xlwt
- openpyxl
- statsmodels
- linearmodels
- black
- pip:
- jupyter-book>=0.9.1
- pandas-datareader
- ogcore
126 changes: 126 additions & 0 deletions examples/run_og_zaf.py
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# Need to fix references to Calculator, reform json, and substitute new tax
# function call
import multiprocessing
from distributed import Client
import os
import json
import time
# from taxcalc import Calculator
from ogzaf.calibrate import Calibration
from ogcore.parameters import Specifications
from ogcore import output_tables as ot
from ogcore import output_plots as op
from ogcore.execute import runner
from ogcore.utils import safe_read_pickle


def main():
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 7)
print("Number of workers = ", num_workers)

# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, "OG-ZAF-Example", "OUTPUT_BASELINE")
reform_dir = os.path.join(CUR_DIR, "OG-ZAF-Example", "OUTPUT_REFORM")

"""
---------------------------------------------------------------------------
Run baseline policy
---------------------------------------------------------------------------
"""
# Set up baseline parameterization
p = Specifications(
baseline=True,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=base_dir,
)
# Update parameters for baseline from default json file
p.update_specifications(
json.load(
open(
os.path.join(
CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json"
)
)
)
)

# Run model
start_time = time.time()
runner(p, time_path=True, client=client)
print("run time = ", time.time() - start_time)

"""
---------------------------------------------------------------------------
Run reform policy
---------------------------------------------------------------------------
"""

# create new Specifications object for reform simulation
p2 = Specifications(
baseline=False,
num_workers=num_workers,
baseline_dir=base_dir,
output_base=reform_dir,
)
# Update parameters for baseline from default json file
p2.update_specifications(
json.load(
open(
os.path.join(
CUR_DIR, "..", "ogzaf", "ogzaf_default_parameters.json"
)
)
)
)
# additional parameters to change
updated_params = {
"cit_rate": [[0.35]],
}
p2.update_specifications(updated_params)
# Run model
start_time = time.time()
runner(p2, time_path=True, client=client)
print("run time = ", time.time() - start_time)
client.close()

"""
---------------------------------------------------------------------------
Save some results of simulations
---------------------------------------------------------------------------
"""
base_tpi = safe_read_pickle(os.path.join(base_dir, "TPI", "TPI_vars.pkl"))
base_params = safe_read_pickle(os.path.join(base_dir, "model_params.pkl"))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, "TPI", "TPI_vars.pkl")
)
reform_params = safe_read_pickle(
os.path.join(reform_dir, "model_params.pkl")
)
ans = ot.macro_table(
base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=["Y", "C", "K", "L", "r", "w"],
output_type="pct_diff",
num_years=10,
start_year=base_params.start_year,
)

# create plots of output
op.plot_all(
base_dir, reform_dir, os.path.join(CUR_DIR, "OG-ZAF_example_plots")
)

print("Percentage changes in aggregates:", ans)
# save percentage change output to csv file
ans.to_csv("ogind_example_output.csv")


if __name__ == "__main__":
# execute only if run as a script
main()
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