Linear Models
Linear (regression) models for Python. Extends statsmodels with Panel regression, instrumental variable estimators, system estimators and models for estimating asset prices:
Panel models:
Fixed effects (maximum two-way)
First difference regression
Between estimator for panel data
Pooled regression for panel data
Fama-MacBeth estimation of panel models
Instrumental Variable estimators
Two-stage Least Squares
Limited Information Maximum Likelihood
k-class Estimators
Generalized Method of Moments, also with continuously updating
Factor Asset Pricing Models:
2- and 3-step estimation
Time-series estimation
GMM estimation
System Regression:
Seemingly Unrelated Regression (SUR/SURE)
Three-Stage Least Squares (3SLS)
Designed to work equally well with NumPy, Pandas or xarray data.
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.
Installing
The latest release can be installed using pip
pip install linearmodelsThe master branch can be installed by cloning the repo and running setup
git clone https://github.com/bashtage/linearmodels
cd linearmodels
python setup.py installDocumentation
Stable Documentation is built on every tagged version using doctr. Development Documentation is automatically built on every successful build of master.
Plan and status
Should eventually add some useful linear model estimators such as panel regression. Currently only the single variable IV estimators are polished.
Linear Instrumental variable estimation - complete
Linear Panel model estimation - complete
Fama-MacBeth regression - complete
Linear Factor Asset Pricing - complete
System regression - complete
Linear IV Panel model estimation - not started
Dynamic Panel model estimation - not started
Requirements
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 (0.18+)
pandas (0.19+)
statsmodels (0.8+)
xarray (0.9+, optional)
Testing
py.test
Documentation
sphinx
guzzle_sphinx_theme
nbsphinx
nbconvert
nbformat
ipython
jupyter
1 maintainer
Release files for linearmodels 4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| linearmodels-4.1.tar.gz | 1.5 MB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| linearmodels-4.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 2.3 MB
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