%global _empty_manifest_terminate_build 0 Name: python-linearmodels Version: 4.27 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 Requires: python3-numpy Requires: python3-pandas Requires: python3-scipy Requires: python3-statsmodels Requires: python3-property-cached Requires: python3-mypy-extensions Requires: python3-Cython Requires: python3-pyhdfe Requires: python3-formulaic Requires: python3-setuptools-scm %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 %package -n python3-linearmodels Summary: Linear Panel, Instrumental Variable, Asset Pricing, and System Regression models for Python Provides: python-linearmodels BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip 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 %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 %prep %autosetup -n linearmodels-4.27 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-linearmodels -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 4.27-1 - Package Spec generated