From 94e121e7cbab380533542e80ca49393aa8f05950 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Sun, 23 Apr 2023 02:32:07 +0000 Subject: automatic import of python-linearmodels --- python-linearmodels.spec | 976 +++++++++++++++++++++++------------------------ 1 file changed, 488 insertions(+), 488 deletions(-) (limited to 'python-linearmodels.spec') diff --git a/python-linearmodels.spec b/python-linearmodels.spec index 9534c9b..61f0931 100644 --- a/python-linearmodels.spec +++ b/python-linearmodels.spec @@ -1,11 +1,11 @@ %global _empty_manifest_terminate_build 0 Name: python-linearmodels -Version: 4.27 +Version: 4.29 Release: 1 Summary: Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python License: NCSA URL: http://github.com/bashtage/linearmodels -Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ab/db/dcd2f13a06cb7537b65464e3632f731ff6f4db344e0b9d6c8d8e3d95fe62/linearmodels-4.27.tar.gz +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b7/f4/c60c761f36efd875f5bd8df54f66e34f812bd472f960b2d5e6740c5b0fa1/linearmodels-4.29.tar.gz Requires: python3-numpy Requires: python3-pandas @@ -16,170 +16,170 @@ Requires: python3-mypy-extensions Requires: python3-Cython Requires: python3-pyhdfe Requires: python3-formulaic -Requires: python3-setuptools-scm +Requires: python3-setuptools-scm[toml] %description -# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | -| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | -| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | -| | [![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 -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. - -Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) -package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). - -## 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 -pip 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 (3.8+ 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 3.8+ -- NumPy (1.18+) -- SciPy (1.3+) -- pandas (1.0+) -- statsmodels (0.12+) -- xarray (0.16+, optional) -- Cython (0.29.24+, optional) - -### Testing - -- py.test - -### Documentation - -- sphinx -- sphinx-material -- nbsphinx -- nbconvert -- nbformat -- ipython -- jupyter +# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | +| | [![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 +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. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## 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 +pip 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 (3.8+ 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 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter %package -n python3-linearmodels @@ -192,338 +192,338 @@ BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-linearmodels -# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | -| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | -| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | -| | [![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 -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. - -Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) -package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). - -## 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 -pip 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 (3.8+ 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 3.8+ -- NumPy (1.18+) -- SciPy (1.3+) -- pandas (1.0+) -- statsmodels (0.12+) -- xarray (0.16+, optional) -- Cython (0.29.24+, optional) - -### Testing - -- py.test - -### Documentation - -- sphinx -- sphinx-material -- nbsphinx -- nbconvert -- nbformat -- ipython -- jupyter +# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | +| | [![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 +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. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## 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 +pip 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 (3.8+ 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 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter %package help Summary: Development documents and examples for linearmodels Provides: python3-linearmodels-doc %description help -# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | -| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | -| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | -| | [![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 -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. - -Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) -package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). - -## 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 -pip 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 (3.8+ 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 3.8+ -- NumPy (1.18+) -- SciPy (1.3+) -- pandas (1.0+) -- statsmodels (0.12+) -- xarray (0.16+, optional) -- Cython (0.29.24+, optional) - -### Testing - -- py.test - -### Documentation - -- sphinx -- sphinx-material -- nbsphinx -- nbconvert -- nbformat -- ipython -- jupyter +# 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://dev.azure.com/kevinksheppard/kevinksheppard/_apis/build/status/bashtage.linearmodels?branchName=main)](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) | +| | [![Build status](https://ci.appveyor.com/api/projects/status/7768doy6wrdunmdt/branch/main?svg=true)](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) | +| **Coverage** | [![codecov](https://codecov.io/gh/bashtage/linearmodels/branch/main/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-main) | +| | [![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 +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. + +Formula support comes from the [formulaic](https://github.com/matthewwardrop/formulaic/) +package which is a replacement for [patsy](https://patsy.readthedocs.io/en/latest/). + +## 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 +pip 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 (3.8+ 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 3.8+ +- NumPy (1.18+) +- SciPy (1.3+) +- pandas (1.0+) +- statsmodels (0.12+) +- xarray (0.16+, optional) +- Cython (0.29.24+, optional) + +### Testing + +- py.test + +### Documentation + +- sphinx +- sphinx-material +- nbsphinx +- nbconvert +- nbformat +- ipython +- jupyter %prep -%autosetup -n linearmodels-4.27 +%autosetup -n linearmodels-4.29 %build %py3_build @@ -563,5 +563,5 @@ mv %{buildroot}/doclist.lst . %{_docdir}/* %changelog -* Mon Apr 10 2023 Python_Bot - 4.27-1 +* Sun Apr 23 2023 Python_Bot - 4.29-1 - Package Spec generated -- cgit v1.2.3