Panel models

Like statsmodels to include, supports patsy formulas for specifying models. For example, the classic Grunfeld regression can be specified

import numpy as np
from statsmodels.datasets import grunfeld
data = grunfeld.load_pandas().data
data.year = data.year.astype(np.int64)
# MultiIndex, entity - time
data = data.set_index(['firm','year'])
from linearmodels import PanelOLS
mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True)
res = mod.fit(cov_type='clustered', cluster_entity=True)

Models can also be specified using the formula interface.

from linearmodels import PanelOLS
mod = PanelOLS.from_formula('invest ~ value + capital + EntityEffect', data)
res = mod.fit(cov_type='clustered', cluster_entity=True)

The formula interface for PanelOLS supports the special values EntityEffects and TimeEffects which add entity (fixed) and time effects, respectively.

Instrumental Variable Models

IV regression models can be similarly specified.

import numpy as np
from linearmodels.iv import IV2SLS
from linearmodels.datasets import mroz
data = mroz.load()
mod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)

The expressions in the [ ] indicate endogenous regressors (before ~) and the instruments.

pip install linearmodels

git clone https://github.com/bashtage/linearmodels
cd linearmodels
python setup.py install

Running

With the exception of Python 3.5+, which is a hard requirement, the others are the version that are being used in the test environment. It is possible that older versions work.

• Python 3.5+: extensive use of @ operator

• NumPy (1.11+)

• SciPy (0.17+)

• Pandas (0.19+)

• xarray (0.9+)

• Statsmodels (0.8+)

Testing

• py.test

Documentation

• sphinx

• sphinx_rtd_theme

• nbsphinx

• nbconvert

• nbformat

• ipython

• jupyter