Metadata-Version: 1.1
Name: linearmodels
Version: 3.5
Summary: Instrumental Variable and Linear Panel models for Python
Home-page: http://github.com/bashtage/linearmodels
Author: Kevin Sheppard
Author-email: kevin.k.sheppard@gmail.com
License: NCSA
Description: Linear Models

        =============

        

        |Build Status| |codecov|

        

        Linear (regression) models for Python. Extends

        `statsmodels <http://www.statsmodels.org>`__ to include Panel regression

        and instrumental variable estimators:

        

        -  **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)

        

        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

        

        .. code:: 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_effect=True)

            res = mod.fit(cov_type='clustered', cluster_entity=True)

        

        Models can also be specified using the formula interface.

        

        .. code:: python

        

            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.

        

        .. code:: 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

        

        .. code:: bash

        

            pip install linearmodels

        

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

        

        .. code:: bash

        

            git clone https://github.com/bashtage/linearmodels

            cd linearmodels

            python setup.py install

        

        Documentation

        -------------

        

        `Stable Documentation <https://bashtage.github.io/linearmodels/doc>`__

        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 - **partially complete** (3SLS not started)

        -  Linear IV Panel model estimation - *not started*

        

        Requirements

        ------------

        

        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 (0.17+)

        -  pandas (0.18+)

        -  xarray (0.9+)

        -  statsmodels (0.8+)

        

        Testing

        ~~~~~~~

        

        -  py.test

        

        Documentation

        ~~~~~~~~~~~~~

        

        -  sphinx

        -  guzzle\_sphinx\_theme

        -  nbsphinx

        -  nbconvert

        -  nbformat

        -  ipython

        -  jupyter

        

        .. |Build Status| image:: https://travis-ci.org/bashtage/linearmodels.svg?branch=master

           :target: https://travis-ci.org/bashtage/linearmodels

        .. |codecov| image:: https://codecov.io/gh/bashtage/linearmodels/branch/master/graph/badge.svg

           :target: https://codecov.io/gh/bashtage/linearmodels

        
Keywords: linear models,regression,instrumental variables,IV,panel,fixed effects,clustered,heteroskedasticity,endogeneity,instruments,statistics,statistical inference,econometrics
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
