Metadata-Version: 2.1
Name: grmpy
Version: 0.1.9
Summary: grmpy is a Python package for the simulation and estimation of the generalized Roy model.
Home-page: http://grmpy.readthedocs.io
Author: The grmpy Development Team
Author-email: eisenhauer@policy-lab.org
License: MIT
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: kernreg
Requires-Dist: linearmodels
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: oyaml
Requires-Dist: pandas
Requires-Dist: pytest
Requires-Dist: scikit-learn
Requires-Dist: scikit-misc
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: statsmodels

grmpy is a Python package for the simulation and estimation of the generalized Roy model.

# grmpy

``grmpy``  is an open-source Python package for the simulation and estimation of the generalized Roy model. It serves as a teaching tool to promote the conceptual framework of the generalized Roy model, illustrate a variety of issues in the econometrics of policy evaluation, and showcases basic software engineering practices. <br>
Marginal Treatment Effects (MTE) can be estimated based on a parametric normal model or,
alternatively, via the semiparametric method of Local Instrumental Variables (LIV).

You can install ```grmpy``` either via pip

```
$ pip install grmpy
```
Or download it directly from our GitHub repository and install the package in editable mode

```
$ git clone https://github.com/OpenSourceEconomics/grmpy.git
$ pip install -e .
```

---
## Quick Start
> Initialization File

```grmpy``` relies on an ```"initialization.yml"``` file (referred to as ``ìnit_file`` below)
to perform both simulation and estimation.
For example, check out these two ``init_files`` for
[simulation and parametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial.grmpy.yml) as well as 
a [semiparametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial_semipar.yml) setup.

Below you'll find some example code you can copy to jump-start your project.  

> Simulation
```
import grmpy

# Specify the initilaization file you want to use, e.g.:
init_file = "ProjectFiles/simulation.yml"

data = grmpy.simulate(init_file)
```
> Estimation

```
import grmpy

# Specify the initilaization file you want to use, e.g.:
init_file = "ProjectFiles/estimation.yml"

# Parametric Normal Model
rslt = grmpy.fit(init_file, semipar=False)
grmpy.plot_mte(rslt, init_file, color="blue", semipar=False, save_plot="MTE_par.png")

# Local Instrumental Variables (Semiparametric Model)
rslt = grmpy.fit(init_file, semipar=True)
grmpy.plot_mte(rslt, init_file, color="orange", semipar=True, nboot= 250, save_plot="MTE_semipar.png")
```

Please visit our [online documentation](http://grmpy.readthedocs.io/) for tutorials and more.

-----
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