Metadata-Version: 2.1
Name: linearmodels
Version: 4.24
Summary: Linear Panel, Instrumental Variable, Asset Pricing, and System Regression 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
        
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        | **Latest Release**         | [![PyPI version](https://badge.fury.io/py/linearmodels.svg)](https://badge.fury.io/py/linearmodels)                                                                                                                                                      |
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        | **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 main 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 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
        
        With the exception of Python 3 (3.7+ tested), 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 3.7+
        - NumPy (1.15+)
        - SciPy (1.3+)
        - pandas (0.25+)
        - statsmodels (0.11+)
        - xarray (0.13+, optional)
        - Cython (0.29.21+, optional)
        
        ### Testing
        
        - py.test
        
        ### Documentation
        
        - sphinx
        - sphinx-material
        - nbsphinx
        - nbconvert
        - nbformat
        - ipython
        - jupyter
        
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 :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
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
Requires-Python: >=3.6
Description-Content-Type: text/markdown
