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
High-dimensional Regresssion:
- Absorbing Least Squares
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)
- Generalized Method of Moments (GMM) System Estimation

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_effects=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 + EntityEffects', 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 linearmodels

The master branch can be installed by cloning the repo and running setup

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

Documentation

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 3.6+, 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 3.6+
NumPy (1.14+)
SciPy (1+)
pandas (0.22+)
statsmodels (0.9+)
xarray (0.9+, optional)
cython (optional)

Testing

py.test

Documentation

sphinx
sphinx-material
nbsphinx
nbconvert
nbformat
ipython
jupyter

Metadata

Release files for linearmodels 4.15.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for linearmodels 4.15.4
File	Size	Uploaded
linearmodels-4.15.4.tar.gz	1.7 MB	Jan 10, 2020	Details

Built distributions (wheels)

Table of built distributions (wheels) for linearmodels 4.15.4
File	Interpreter	ABI	Platform	Reset
linearmodels-4.15.4-cp38-cp38-win_amd64.whl 1.6 MB Jan 10, 2020	CPython 3.8	CPython 3.8	Windows x86-64	Details
linearmodels-4.15.4-cp38-cp38-win32.whl 1.6 MB Jan 10, 2020	CPython 3.8	CPython 3.8	Windows x86-32	Details
linearmodels-4.15.4-cp38-cp38-manylinux1_x86_64.whl 1.5 MB Jan 10, 2020	CPython 3.8	CPython 3.8	Linux glibc 2.5+ x86-64	Details
linearmodels-4.15.4-cp38-cp38-manylinux1_i686.whl 1.4 MB Jan 10, 2020	CPython 3.8	CPython 3.8	Linux glibc 2.5+ x86-32	Details
linearmodels-4.15.4-cp38-cp38-macosx_10_9_x86_64.whl 1.4 MB Jan 10, 2020	CPython 3.8	CPython 3.8	macOS 10.9+ x86-64	Details
linearmodels-4.15.4-cp37-cp37m-win_amd64.whl 1.6 MB Jan 10, 2020	CPython 3.7	CPython 3.7 pymalloc	Windows x86-64	Details
linearmodels-4.15.4-cp37-cp37m-win32.whl 1.6 MB Jan 10, 2020	CPython 3.7	CPython 3.7 pymalloc	Windows x86-32	Details
linearmodels-4.15.4-cp37-cp37m-manylinux1_x86_64.whl 1.5 MB Jan 10, 2020	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.5+ x86-64	Details
linearmodels-4.15.4-cp37-cp37m-manylinux1_i686.whl 1.4 MB Jan 10, 2020	CPython 3.7	CPython 3.7 pymalloc	Linux glibc 2.5+ x86-32	Details
linearmodels-4.15.4-cp37-cp37m-macosx_10_9_x86_64.whl 1.4 MB Jan 10, 2020	CPython 3.7	CPython 3.7 pymalloc	macOS 10.9+ x86-64	Details
linearmodels-4.15.4-cp36-cp36m-win_amd64.whl 1.6 MB Jan 10, 2020	CPython 3.6	CPython 3.6 pymalloc	Windows x86-64	Details
linearmodels-4.15.4-cp36-cp36m-win32.whl 1.6 MB Jan 10, 2020	CPython 3.6	CPython 3.6 pymalloc	Windows x86-32	Details
linearmodels-4.15.4-cp36-cp36m-manylinux1_x86_64.whl 1.5 MB Jan 10, 2020	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-64	Details
linearmodels-4.15.4-cp36-cp36m-manylinux1_i686.whl 1.4 MB Jan 10, 2020	CPython 3.6	CPython 3.6 pymalloc	Linux glibc 2.5+ x86-32	Details
linearmodels-4.15.4-cp36-cp36m-macosx_10_9_x86_64.whl 1.4 MB Jan 10, 2020	CPython 3.6	CPython 3.6 pymalloc	macOS 10.9+ x86-64	Details

Total release size: 24.1 MB

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