# linearmodels

**Repository Path**: jerryyang66/linearmodels

## Basic Information

- **Project Name**: linearmodels
- **Description**: Add linear models including instrumental variable and panel data models that are missing from statsmodels.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-10-29
- **Last Updated**: 2024-10-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Linear Models

| Metric                     |                                                                                                                                                                                                                                                          |
| :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Latest Release**         | [![PyPI version](https://badge.fury.io/py/linearmodels.svg)](https://badge.fury.io/py/linearmodels)                                                                                                                                                      |
| **Continuous Integration** | [![Build Status](https://travis-ci.org/bashtage/linearmodels.svg?branch=master)](https://travis-ci.org/bashtage/linearmodels)                                                                                                                            |
|                            | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/master?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/master)                                                                              |
| **Coverage**               | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/master/graph/badge.svg)](https://codecov.io/gh/bashtage/linearmodels)                                                                                                                     |
| **Code Quality**           | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/745a24a69cb2466b95df6a53c83892de)](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |
|                            | [![codebeat badge](https://codebeat.co/badges/aaae2fb4-72b5-4a66-97cd-77b93488f243)](https://codebeat.co/projects/github-com-bashtage-linearmodels-master)                                                                                               |
|                            | [![Code Quality: Python](https://img.shields.io/lgtm/grade/python/g/bashtage/linearmodels.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/linearmodels/context:python)                                                                 |
|                            | [![Total Alerts](https://img.shields.io/lgtm/alerts/g/bashtage/linearmodels.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/bashtage/linearmodels/alerts)                                                                                       |
| **Citation**               | [![DOI](https://zenodo.org/badge/82291672.svg)](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
[patsy](https://patsy.readthedocs.io/en/latest/) 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.

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

```bash
git clone https://github.com/bashtage/linearmodels
cd linearmodels
python setup.py 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 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 (1.15+)
- SciPy (1.0.1+)
- pandas (0.23+)
- statsmodels (0.9+)
- xarray (0.10+, optional)
- Cython (0.29.14+, optional)

### Testing

- py.test

### Documentation

- sphinx
- sphinx-material
- nbsphinx
- nbconvert
- nbformat
- ipython
- jupyter