Metadata-Version: 2.1
Name: econpizza
Version: 0.1.5
Summary: Solve nonlinear perfect foresight models
Home-page: https://github.com/gboehl/econpizza
Author: Gregor Boehl
Author-email: admin@gregorboehl.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numba
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: jax
Requires-Dist: jaxlib
Requires-Dist: grgrlib (>=0.1.5)


econpizza
=========

**Simulate nonlinear perfect foresight models in Python using AD**

.. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg
    :target: https://github.com/gboehl/econpizza/actions?query=workflow%3ATests
.. image:: https://badge.fury.io/py/econpizza.svg
    :target: https://badge.fury.io/py/econpizza
.. image:: https://img.shields.io/badge/GitHub-gboehl%2Feconpizza-blue.svg?style=flat
    :target: https://github.com/gboehl/econpizza
.. image:: https://readthedocs.org/projects/econpizza/badge/?version=latest
    :target: http://econpizza.readthedocs.io/en/latest/?badge=latest    

The baseline mechanism is a Newton-based stacking method in the spirit of Boucekkine (1995), Juillard (1996) and others. It is hence similar to the solver in dynare, but faster and more robust due to the use of automatic differentiation and sparse jacobians. Even IRFs for large-scale models with occassionally binding constraints can be computed in less than a second.

The package makes heavy use of `automatic differentiation <https://en.wikipedia.org/wiki/Automatic_differentiation>`_ via `jax <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_!

There is some experimental, undocumented support to solve nonlinear HANK models. The approach to deal with the distribution is inspired by the `Sequence-Space Jacobian <https://github.com/shade-econ/sequence-jacobian>`_ method (`Auclert et al., 2022, ECMA <https://doi.org/10.3982/ECTA17434>`_).

Installation
-------------

Installing the `repository version <https://pypi.org/project/econpizza/>`_ from PyPi is as simple as:

.. code-block:: bash

   pip install econpizza
  
The most recent version from GitHub with all **experimental features** can be installed via

.. code-block:: bash

   pip install git+https://github.com/gboehl/grgrlib
   pip install git+https://github.com/gboehl/econpizza

Note that the latter requires `git <https://www.activestate.com/resources/quick-reads/pip-install-git/#:~:text=To%20install%20Git%20for%20Windows,installer%20and%20follow%20the%20steps.>`_ to be installed.

Documentation
-------------

There is some `documentation <https://econpizza.readthedocs.io/en/latest/tutorial.html>`_ out there.

Citation
--------

**econpizza** is developed by Gregor Boehl to simulate nonlinear perfect foresight models. Please cite it with

.. code-block::

    @Misc{boehl2022pizza,
    title         = {Econpizza: Solve all sorts of nonlinear perfect foresight models},
    author        = {Boehl, Gregor},
    howpublished  = {\url{https://github.com/gboehl/econpizza}},
    year = {2022}
    }

For the Boehl-Hommes method:

.. code-block::

    @techreport{boehl2021rational,
    title         = {Rational vs. Irrational Beliefs in a Complex World},
    author        = {Boehl, Gregor and Hommes, Cars},
    year          = 2021,
    institution   = {IMFS Working Paper Series}
    }


I appreciate citations for **econpizza** because it helps me to find out how people have been using the package and it motivates further work.


References
----------

Boehl, Gregor and Hommes, Cars (2021). `Rational vs. Irrational Beliefs in a Complex World <https://gregorboehl.com/live/rational_chaos_bh.pdf>`_. *IMFS Working papers*


