# linearmodels 4.5

Instrumental Variable and Linear Panel models for Python

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)
• 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.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
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

## 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)

• py.test

## Documentation

• sphinx
• guzzle_sphinx_theme
• nbsphinx
• nbconvert
• nbformat
• ipython
• jupyter

File Type Py Version Uploaded on Size
Python Wheel py2.py3 2017-12-11 886KB
Source 2017-12-11 975KB
• Author: Kevin Sheppard