econpizza 0.1.11

Solve nonlinear perfect foresight models

Solve nonlinear heterogeneous agent models using machine learning techniques

Econpizza is a framework to solve and simulate nonlinear perfect foresight models, with or without heterogeneous agents. A parser allows to express economic models in a simple, high-level fashion as yaml-files. Additionally, generic and robust routines for steady state search are provided.

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

The package makes heavy use of automatic differentiation via Jax.

Econpizza can solve nonlinear HANK models. The approach to deal with the distribution is inspired by the Sequence-Space Jacobian method (Auclert et al., 2022, ECMA). Steady state and nonlinear impulse responses (including, e.g., the ELB) can typically be found within a few seconds.

There is a model parser to allow for the simple and generic specification of models (with or without heterogeneity).

Installation

Installing the repository version from PyPi is as simple as:

pip install econpizza

Alternatively, the most recent version from GitHub with some experimental features can be installed via

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

Note that the latter requires git to be installed.

Econpizza needs Jax to be installed. This is not a problem for MacOS and Linux, but the time for Jax to fully support Windows has not yet come. Following the (somewhat cryptic) guide, one could try

pip install jax[cpu]==0.3.7 -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver

which works for GitHub Actions (but seems to cause problems later).

Documentation

The Documentation and tutorials can be found here.

References

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

@Misc{boehl2022pizza,
title         = {Econpizza: solving nonlinear heterogeneous agents models using machine learning techniques},
author        = {Boehl, Gregor},
howpublished  = {\url{https://econpizza.readthedocs.io/_/downloads/en/latest/pdf/}},
year = {2022}
}

For the Boehl-Hommes method: Boehl, Gregor and Hommes, Cars (2021). Rational vs. Irrational Beliefs in a Complex World. IMFS Working papers

@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.