Metadata-Version: 2.4
Name: dcegm
Version: 0.1.3
Summary: Python package for solving and simulating finite-horizon stochastic discrete-continuous dynamic choice models using the DC-EGM algorithm from Iskhakov, Jørgensen, Rust, and Schjerning (QE, 2017).
Project-URL: Github, https://github.com/OpenSourceEconomics/dcegm
Author-email: "Max Blesch, Sebastian Gsell" <gsell.sebastian@gmail.com>
Maintainer-email: "Max Blesch, Sebastian Gsell" <maxblesch@gmail.com>
License: MIT
License-File: LICENSE
Keywords: Computational Economics,DC-EGM,Dynamic Programming,Econometrics,Economics,Life Cycle Models,Statistics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Requires-Dist: jax
Requires-Dist: numpy
Requires-Dist: upper-envelope
Description-Content-Type: text/markdown

# dcegm

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<!--
Python implementation of the Discrete-Continuous Endogenous Grid Method (DC-EGM) for
solving dynamic stochastic lifecycle models of continuous (e.g. consumption-savings) and
additional discrete choices. -->

**Note:** This is a pre-release version of the package. While the core features are in
place, the interface and functionality may still evolve. Feedback and contributions are
welcome.

`dcegm` is a Python package for solving and simulating finite-horizon stochastic
discrete-continuous dynamic choice models using the DC-EGM algorithm from Iskhakov,
Jørgensen, Rust, and Schjerning (QE, 2017).

The solution algorithm employs an extension of the Fast Upper-Envelope Scan (FUES) from
Dobrescu & Shanker (2022).

## Installation

You can install `dcegm` via PyPI or directly from GitHub. In your terminal, run:

```console
$ pip install dcegm
```

To install the latest development version directly from the GitHub repository, run:

```console
$ pip install git+https://github.com/OpenSourceEconomics/dcegm.git
```

## Documentation

The documentation is hosted at https://dcegm.readthedocs.io

## References

1. Christopher D. Carroll (2006).
   [The method of endogenous gridpoints for solving dynamic stochastic optimization problems](http://www.sciencedirect.com/science/article/pii/S0165176505003368).
   *Economics Letters*
1. Iskhakov, Jorgensen, Rust, & Schjerning (2017).
   [The Endogenous Grid Method for Discrete-Continuous Dynamic Choice Models with (or without) Taste Shocks](http://onlinelibrary.wiley.com/doi/10.3982/QE643/full).
   *Quantitative Economics*
1. Loretti I. Dobrescu & Akshay Shanker (2022).
   [Fast Upper-Envelope Scan for Discrete-Continuous Dynamic Programming](https://dx.doi.org/10.2139/ssrn.4181302).

<!-- ## Citation

If you use dcegm for your research, please do not forget to cite it.

```bibtex
@Unpublished{BleschGsell2025,
   Title = {dcegm: A GPU-accelerated python implementation of the Discrete-Contiunous Endogenous Grid Method},
   Author = {Maximilian Blesch & Sebastian Gsell},
   Year = {2025},
   Url = {https://github.com/OpenSourceEconomics/dcegm} }
``` -->
