Metadata-Version: 2.1
Name: pytwoway
Version: 0.0.1
Summary: Two way fixed effect models in python
Home-page: https://github.com/tlamadon/pytwoway
Author: Thibaut Lamadon, Adam Alexander Oppenheimer
Maintainer: Thibaut Lamadon
License: Apache Software License 
Project-URL: Documentation, https://github.com/tlamadon/pytwoway
Project-URL: Source, https://github.com/tlamadon/pytwoway
Project-URL: Tracker, https://github.com/tlamadon/pytwoway/issues
Keywords: sparse,two way fixed effects
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha 
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: networkx (>=2.3)
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: pyamg
Requires-Dist: pyreadr
Requires-Dist: tqdm
Requires-Dist: decorator

# pytwoway
Two way fixed effect models in python

To run using default options:
- If you have data
```python
from pytwoway import twfe_network as tn
# data is labor data, formatting is 'long' or 'event study', and col_dict gives a dictionary of column names:
d_net = tn.twfe_network(data, formatting, col_dict)
# Convert long data into event study data (not necessary if the data is already in event study format):
d_net.refactor_es()
# Run the bias-corrected AKM estimator:
d_net.run_akm_corrected()
# Cluster firms based on their wage CDFs (required for the CRE estimator)
d_net.cluster()
# Run the CRE estimator
d_net.run_cre()
```

- If you want to simulate data
```python
from pytwoway import twfe_network as tn
# Parameters are optional when simulating data:
d_net = tn.twfe_network()
# Convert long data into event study data (this is necessary for simulated data):
d_net.refactor_es()
# Run the bias-corrected AKM estimator:
d_net.run_akm_corrected()
# Cluster firms based on their wage CDFs (required for the CRE estimator)
d_net.cluster()
# Run the CRE estimator
d_net.run_cre()
```


