# 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
Raw data
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"description": "# Linear Models\n\n| Metric | |\n| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| **Latest Release** | [](https://badge.fury.io/py/linearmodels) |\n| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) |\n| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) |\n| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |\n| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) |\n| **Citation** | [](https://zenodo.org/badge/latestdoi/82291672) |\n\nLinear (regression) models for Python. Extends\n[statsmodels](http://www.statsmodels.org) with Panel regression,\ninstrumental variable estimators, system estimators and models for\nestimating asset prices:\n\n- **Panel models**:\n - Fixed effects (maximum two-way)\n - First difference regression\n - Between estimator for panel data\n - Pooled regression for panel data\n - Fama-MacBeth estimation of panel models\n\n- **High-dimensional Regresssion**:\n - Absorbing Least Squares\n\n- **Instrumental Variable estimators**\n - Two-stage Least Squares\n - Limited Information Maximum Likelihood\n - k-class Estimators\n - Generalized Method of Moments, also with continuously updating\n\n- **Factor Asset Pricing Models**:\n - 2- and 3-step estimation\n - Time-series estimation\n - GMM estimation\n\n- **System Regression**:\n - Seemingly Unrelated Regression (SUR/SURE)\n - Three-Stage Least Squares (3SLS)\n - Generalized Method of Moments (GMM) System Estimation\n\nDesigned to work equally well with NumPy, Pandas or xarray data.\n\n## Panel models\n\nLike [statsmodels](http://www.statsmodels.org) to include, supports\nformulas for specifying models. For example, the classic Grunfeld regression can be\nspecified\n\n```python\nimport numpy as np\nfrom statsmodels.datasets import grunfeld\ndata = grunfeld.load_pandas().data\ndata.year = data.year.astype(np.int64)\n# MultiIndex, entity - time\ndata = data.set_index(['firm','year'])\nfrom linearmodels import PanelOLS\nmod = PanelOLS(data.invest, data[['value','capital']], entity_effects=True)\nres = mod.fit(cov_type='clustered', cluster_entity=True)\n```\n\nModels can also be specified using the formula interface.\n\n```python\nfrom linearmodels import PanelOLS\nmod = PanelOLS.from_formula('invest ~ value + capital + EntityEffects', data)\nres = mod.fit(cov_type='clustered', cluster_entity=True)\n```\n\nThe formula interface for `PanelOLS` supports the special values\n`EntityEffects` and `TimeEffects` which add entity (fixed) and time\neffects, respectively.\n\nFormula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/)\npackage which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/).\n\n## Instrumental Variable Models\n\nIV regression models can be similarly specified.\n\n```python\nimport numpy as np\nfrom linearmodels.iv import IV2SLS\nfrom linearmodels.datasets import mroz\ndata = mroz.load()\nmod = IV2SLS.from_formula('np.log(wage) ~ 1 + exper + exper ** 2 + [educ ~ motheduc + fatheduc]', data)\n```\n\nThe expressions in the `[ ]` indicate endogenous regressors (before `~`)\nand the instruments.\n\n## Installing\n\nThe latest release can be installed using pip\n\n```bash\npip install linearmodels\n```\n\nThe main branch can be installed by cloning the repo and running setup\n\n```bash\ngit clone https://github.com/bashtage/linearmodels\ncd linearmodels\npip install .\n```\n\n## Documentation\n\n[Stable Documentation](https://bashtage.github.io/linearmodels/) is\nbuilt on every tagged version using\n[doctr](https://github.com/drdoctr/doctr).\n[Development Documentation](https://bashtage.github.io/linearmodels/devel)\nis automatically built on every successful build of main.\n\n## Plan and status\n\nShould eventually add some useful linear model estimators such as panel\nregression. 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