Metadata-Version: 1.1
Name: linearmodels
Version: 4.8
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>`__ 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 <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/>`__ 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 3.5+, 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 (1.12+)
        -  SciPy (0.18+)
        -  pandas (0.20+)
        -  statsmodels (0.8+)
        -  xarray (0.9+, optional)
        
        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
