econpizza 0.4.1

Solve nonlinear perfect foresight models with heterogeneous agents

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. 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 models with heterogeneous agents, including HANK models. The approach to deal with heterogeneity 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.

The philosophy behind this package is to consequently separate model specification (via a yaml file), a high-level interface for model simulation and analysis, and the low-level routines for model solution (which is what happens under the hood).

A model parser allows for the simple and generic specification of models (with or without heterogeneity) in yaml format.

Documentation

The documentation and some tutorials can be found here.

Installation

Installing the repository version from PyPi is as simple as typing

pip install econpizza

in your terminal or Anaconda Prompt. 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.

Installation on Windows

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. Fortunately, there is help out there (see this link for the somewhat cryptic original reference). To install JAX, run

pip install "jax[cpu]===0.3.20" -f https://whls.blob.core.windows.net/unstable/index.html --use-deprecated legacy-resolver

prior to installing Econpizza. Econpizza should then run just fine (proof).

In case you run into an error with ptxas (like in this case), a workaround is to disable CUDA by running the following before importing econpizza or JAX:

import os; os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

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 and Hommes (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.