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|
%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** | [](https://badge.fury.io/py/linearmodels) |
| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) |
| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) |
| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) |
| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |
| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) |
| **Citation** | [](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** | [](https://badge.fury.io/py/linearmodels) |
| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) |
| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) |
| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) |
| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |
| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) |
| **Citation** | [](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** | [](https://badge.fury.io/py/linearmodels) |
| **Continuous Integration** | [](https://dev.azure.com/kevinksheppard/kevinksheppard/_build/latest?definitionId=2&branchName=main) |
| | [](https://ci.appveyor.com/project/bashtage/linearmodels/branch/main) |
| **Coverage** | [](https://codecov.io/gh/bashtage/linearmodels) |
| **Code Quality** | [](https://www.codacy.com/manual/bashtage/linearmodels?utm_source=github.com&utm_medium=referral&utm_content=bashtage/linearmodels&utm_campaign=Badge_Grade) |
| | [](https://codebeat.co/projects/github-com-bashtage-linearmodels-main) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/context:python) |
| | [](https://lgtm.com/projects/g/bashtage/linearmodels/alerts) |
| **Citation** | [](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 <Python_Bot@openeuler.org> - 4.27-1
- Package Spec generated
|
