Skip to main content

Philippines Calibration for OG-Core

Project description

OG-PHL

Org United Nations DESA PSL cataloged OS License: CC0-1.0
Package Python 3.12 Python 3.13 PyPI Latest Release PyPI Downloads
Testing example event parameter example event parameter example event parameter Codecov

OG-PHL is an overlapping-generations (OG) model that allows for dynamic general equilibrium analysis of fiscal policy for the Philippines. OG-PHL 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 Philippines calibration of the model is available at https://eapd-drb.github.io/OG-PHL.

Using and contributing to OG-PHL

  • If you are installing on a Mac computer, install XCode Tools. In Terminal: xcode-select —install
  • Download and install the appropriate Anaconda distribution of Python. Select the correct version for you platform (Windows, Intel Mac, or M1 Mac).
  • In Terminal:
    • 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 ogphl-dev conda environment should not take more than five minutes.
  • Then, conda activate ogphl-dev
  • Then install by pip install -e .

Run an example of the model

  • Navigate to ./examples
  • Run the model with an example reform from terminal/command prompt by typing python run_og_phl.py
  • You can adjust the ./examples/run_og_phl.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-PHL_example_plots
      • This folder will contain a number of plots generated from OG-Core to help you visualize the output from your run
    • ./examples/ogphl_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-PHL-Example/OUTPUT_BASELINE/model_params.pkl
      • Model parameters used in the baseline run
      • See ogcore.execute.py for items in the dictionary object in this pickle file
    • ./examples/OG-PHL-Example/OUTPUT_BASELINE/SS/SS_vars.pkl
      • Outputs from the model steady state solution under the baseline policy
      • See ogcore.SS.py for what is in the dictionary object in this pickle file
    • ./examples/OG-PHL-Example/OUTPUT_BASELINE/TPI/TPI_vars.pkl
      • Outputs from the model timepath solution under the baseline policy
      • See 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 from 35 minutes to more than two hours of compute time.

If you run into errors running the example script, please open a new issue in the OG-PHL 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 ogphl.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-PHL/examples/run_og_phl.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-PHL repository are:

  • Marcelo LaFleur (GitHub handle: @SeaCelo), Senior Economist, Department of Economic and Social Affairs (DESA), United Nations
  • Richard W. Evans (GitHub handle: @rickecon), Senior Economist, Abundance Institute; President, Open Research Group, Inc.
  • Jason DeBacker (GitHub handle: @jdebacker), Associate Professor, University of South Carolina; Vice President of Research, Open Research Group, Inc.

Citing OG-PHL

OG-PHL (Version #.#.#)[Source code], https://github.com/EAPD-DRB/OG-PHL.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ogphl-0.0.13.tar.gz (159.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ogphl-0.0.13-py3-none-any.whl (164.2 kB view details)

Uploaded Python 3

File details

Details for the file ogphl-0.0.13.tar.gz.

File metadata

  • Download URL: ogphl-0.0.13.tar.gz
  • Upload date:
  • Size: 159.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ogphl-0.0.13.tar.gz
Algorithm Hash digest
SHA256 089e2974c4647d28096db060020a7ff10ccfdf5a3076a49fe790cdefdd6ccf4d
MD5 803ee2670c1b53b6901f2d584c085991
BLAKE2b-256 e9a430f04c0f3211c011fee0ba4b232828cddbe506534dfea4884a2a01db1f10

See more details on using hashes here.

File details

Details for the file ogphl-0.0.13-py3-none-any.whl.

File metadata

  • Download URL: ogphl-0.0.13-py3-none-any.whl
  • Upload date:
  • Size: 164.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ogphl-0.0.13-py3-none-any.whl
Algorithm Hash digest
SHA256 9661b4daef5c333945890796c89b95ab7efc955c832226725a77618c8697e059
MD5 72a68d385d3bf2fd0ab5bf1ae28621b5
BLAKE2b-256 46365b7b6a65f26467f40a40d28588cdafd286055abd2ac78791a59a7ddcfad3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page