Linear Models
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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)
- Random effects
- First difference regression
- Between estimator for panel data
- Pooled regression for panel data
- Fama-MacBeth estimation of panel models
- Specification tests: the Hausman test of random effects against
fixed effects and the Pesaran CD test of cross-sectional dependence
-
High-dimensional Regression:
-
Instrumental Variable estimators
- Two-stage Least Squares
- Limited Information Maximum Likelihood
- k-class Estimators
- Generalized Method of Moments, also with continuously updating
- Jackknife IV (JIVE)
- Specification tests: the Cragg-Donald and Kleibergen-Paap rank tests
of the first stage (underidentification)
-
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
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.
Formula support comes from the formulaic
package which is a replacement for patsy.
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.
Installation
The latest release can be installed using pip
pip install linearmodels
Binary wheels are available for Windows, macOS and Linux. Without a wheel, pip
builds linearmodels from source, which requires a C compiler.
The main branch can be installed by cloning the repo and building it with pip.
The build uses meson-python, and pip
installs the other build requirements (Cython, NumPy and setuptools_scm)
automatically.
git clone https://github.com/bashtage/linearmodels
cd linearmodels
pip install .
To work on linearmodels, install the requirements and then build an editable
install
python -m pip install -r requirements.txt -r requirements-dev.txt
python -m pip install -e . --no-build-isolation
Running the tests
The tests are installed with linearmodels. They need pytest 9 and xarray, and
pytest-xdist to run in parallel.
python -c "import linearmodels; linearmodels.test(['--skip-slow', '-n', 'auto'])"
--skip-slow skips the slow tests and --skip-examples the example notebooks.
pytest loads every plugin that is installed in the environment, and the test
configuration of linearmodels is strict, so that, for example, a marker that is
not registered is an error. A plugin that uses such a marker makes tests fail
with a message such as 'gpu' not found in markers configuration option. To run
the tests without the other plugins, turn off their automatic loading and load
pytest-xdist explicitly. This works with python -m pytest too.
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 python -c "import linearmodels; linearmodels.test(['-p', 'xdist', '-n', 'auto'])"
When the tests run in several processes with pytest-xdist, limit the BLAS
libraries to one thread in each process. Otherwise every process starts as many
threads as there are cores, and excessive context switching makes the tests run
very slow. This problem is particularly acute on machines with high CPU counts.
export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export OMP_NUM_THREADS=1
or, in PowerShell,
$env:MKL_NUM_THREADS = 1
$env:OPENBLAS_NUM_THREADS = 1
$env:OMP_NUM_THREADS = 1
Documentation
Stable Documentation is
built by GitHub Actions
when a release is published.
Development Documentation
is built by the same workflow on every push to main.
Plan and status
- 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
linearmodels 7.1 is the last release that supports Python 3.10 and NumPy 1.x
(NumPy < 2). Later releases will raise the minimum supported versions of
Python, NumPy and the other dependencies.
Running
- Python 3.10+
- NumPy (1.23.5+)
- SciPy (1.8+)
- pandas (1.4+)
- statsmodels (0.14.6+)
- formulaic (1.2.1+)
- pyhdfe (0.2.0+)
- mypy_extensions (0.4+)
- packaging
- xarray (0.16+, optional)
Building from source
- A C compiler
- Cython (3.1.0+)
- meson-python
- NumPy (2.0+)
- setuptools_scm (9.2+)
Testing
- pytest (9.0+)
- xarray (0.16+)
- pytest-xdist (optional, to run the tests in parallel)
Documentation
- sphinx
- sphinx-immaterial
- nbsphinx
- nbconvert
- nbformat
- ipython
- jupyter
