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https://github.com/bashtage/linearmodels
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
https://github.com/bashtage/linearmodels
asset-pricing between-estimator clustered-standard-errors fama-macbeth first-difference fixed-effects gmm instrumental-variable iv linear-models ols panel panel-data panel-models panel-regression pooled-ols random-effects regression seemingly-unrelated-regression statistical-model
Last synced: 16 days ago
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Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
- Host: GitHub
- URL: https://github.com/bashtage/linearmodels
- Owner: bashtage
- License: ncsa
- Created: 2017-02-17T11:39:15.000Z (over 7 years ago)
- Default Branch: main
- Last Pushed: 2024-05-31T13:00:29.000Z (23 days ago)
- Last Synced: 2024-06-01T11:13:28.826Z (22 days ago)
- Topics: asset-pricing, between-estimator, clustered-standard-errors, fama-macbeth, first-difference, fixed-effects, gmm, instrumental-variable, iv, linear-models, ols, panel, panel-data, panel-models, panel-regression, pooled-ols, random-effects, regression, seemingly-unrelated-regression, statistical-model
- Language: Python
- Homepage: https://bashtage.github.io/linearmodels/
- Size: 127 MB
- Stars: 899
- Watchers: 26
- Forks: 180
- Open Issues: 50
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Lists
- awesome-meteo - Linear Models
README
# Linear Models
| Metric | |
| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Latest Release** | [](https://badge.fury.io/py/linearmodels) |
| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) |
| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) |
| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |
| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) |
| **Citation** | [](https://zenodo.org/badge/latestdoi/82291672) |
Linear (regression) models for Python. Extends
[statsmodels](http://www.statsmodels.org) 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](http://www.statsmodels.org) to include, supports
formulas for specifying models. For example, the classic Grunfeld regression can be
specified
```python
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.
```python
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](https://github.com/matthewwardrop/formulaic/)
package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/).
## Instrumental Variable Models
IV regression models can be similarly specified.
```python
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
```bash
pip install linearmodels
```
The main branch can be installed by cloning the repo and running setup
```bash
git clone https://github.com/bashtage/linearmodels
cd linearmodels
pip install .
```
## Documentation
[Stable Documentation](https://bashtage.github.io/linearmodels/) is
built on every tagged version using
[doctr](https://github.com/drdoctr/doctr).
[Development Documentation](https://bashtage.github.io/linearmodels/devel)
is automatically built on every successful build of main.
## 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
- Python 3.9+
- NumPy (1.22+)
- SciPy (1.8+)
- pandas (1.4+)
- statsmodels (0.12+)
- formulaic (1.0.0+)
- xarray (0.16+, optional)
- Cython (3.0.10+, optional)
### Testing
- py.test
### Documentation
- sphinx
- sphinx-immaterial
- nbsphinx
- nbconvert
- nbformat
- ipython
- jupyter