Metadata-Version: 2.1
Name: pytwoway
Version: 0.2.7
Summary: Estimate two way fixed effect labor models
Home-page: https://github.com/tlamadon/pytwoway
Author: Thibaut Lamadon
Author-email: thibaut.lamadon@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
License-File: LICENSE

PyTwoWay
--------

.. image:: https://badge.fury.io/py/pytwoway.svg
    :target: https://badge.fury.io/py/pytwoway

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    :target: https://anaconda.org/tlamadon/pytwoway

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    :target: https://anaconda.org/tlamadon/pytwoway

.. image:: https://circleci.com/gh/tlamadon/pytwoway/tree/master.svg?style=shield
    :target: https://circleci.com/gh/tlamadon/pytwoway/tree/master

.. image:: https://img.shields.io/badge/doc-latest-blue
    :target: https://tlamadon.github.io/pytwoway/

.. image:: https://badgen.net/badge//gh/pytwoway?icon=github
    :target: https://github.com/tlamadon/pytwoway

`PyTwoWay` is the Python package associated with the following paper:

"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_" 
by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.  
No. w27368. National Bureau of Economic Research, 2020.

The package provides implementations for a series of estimators for models with two sided heterogeneity:

1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
5. group correlated random effect as presented in the main paper

.. |binder_fe| image:: https://mybinder.org/badge_logo.svg 
    :target: https://mybinder.org/v2/gh/tlamadon/pytwoway/HEAD?filepath=docs%2Fsource%2Fnotebooks%2Ffe_example.ipynb
.. |binder_cre| image:: https://mybinder.org/badge_logo.svg 
    :target: https://mybinder.org/v2/gh/tlamadon/pytwoway/HEAD?filepath=docs%2Fsource%2Fnotebooks%2Fcre_example.ipynb
.. |binder_blm| image:: https://mybinder.org/badge_logo.svg 
    :target: https://mybinder.org/v2/gh/tlamadon/pytwoway/HEAD?filepath=docs%2Fsource%2Fnotebooks%2Fblm_example.ipynb

If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| and for the BLM estimator here: |binder_blm|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.

The code is relatively efficient. Solving large sparse linear models relies on `PyAMG <https://github.com/pyamg/pyamg>`_. This is the code we use to estimate the different decompositions on US data. Data cleaning is handled by `BipartitePandas <https://github.com/tlamadon/bipartitepandas/>`_.

The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.

Quick Start
-----------

To install via pip, from the command line run::

    pip install pytwoway

Authors
-------

Thibaut Lamadon,
Assistant Professor in Economics, University of Chicago,
lamadon@uchicago.edu


Adam A. Oppenheimer,
Research Professional, University of Chicago,
oppenheimer@uchicago.edu

Citation
--------

Please use following citation to cite PyTwoWay in academic publications:

Bibtex entry::

  @techreport{bhlmms2020,
    title={How Much Should We Trust Estimates of Firm Effects and Worker Sorting?},
    author={Bonhomme, St{\'e}phane and Holzheu, Kerstin and Lamadon, Thibaut and Manresa, Elena and Mogstad, Magne and Setzler, Bradley},
    year={2020},
    institution={National Bureau of Economic Research}
  }


Development
-----------

If you want to contribute to the package, the easiest way is to use poetry to set up a local environment::

    poetry install
    poetry run python -m pytest

To push the package to PiP, increase the version number in the `pyproject.toml` file and then::

    poetry build
    poetry publish

Finally to build the package for conda and upload it::

    conda skeleton pypi pytwoway
    conda config --set anaconda_upload yes
    conda-build pytwoway -c tlamadon --output-folder pytwoway
