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%global _empty_manifest_terminate_build 0
Name: python-pytwoway
Version: 0.3.21
Release: 1
Summary: Estimate two way fixed effect labor models
License: MIT License
URL: https://github.com/tlamadon/pytwoway
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/23/9f/6d148b1568ce81513a49c86beed42155b130836882b362a4eae74ad6e437/pytwoway-0.3.21.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-numba
Requires: python3-paramsdict
Requires: python3-bipartitepandas
Requires: python3-scipy
Requires: python3-statsmodels
Requires: python3-pyamg
Requires: python3-qpsolvers
Requires: python3-quadprog
Requires: python3-ConfigArgParse
Requires: python3-matplotlib
Requires: python3-tqdm
%description
`PyTwoWay` is the Python package associated with the following paper:
"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
No. w27368. National Bureau of Economic Research, 2020.
The package provides implementations for a series of estimators for models with two sided heterogeneity:
1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
5. group correlated random effect as presented in the main paper
6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
%package -n python3-pytwoway
Summary: Estimate two way fixed effect labor models
Provides: python-pytwoway
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pytwoway
`PyTwoWay` is the Python package associated with the following paper:
"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
No. w27368. National Bureau of Economic Research, 2020.
The package provides implementations for a series of estimators for models with two sided heterogeneity:
1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
5. group correlated random effect as presented in the main paper
6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
%package help
Summary: Development documents and examples for pytwoway
Provides: python3-pytwoway-doc
%description help
`PyTwoWay` is the Python package associated with the following paper:
"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
No. w27368. National Bureau of Economic Research, 2020.
The package provides implementations for a series of estimators for models with two sided heterogeneity:
1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
5. group correlated random effect as presented in the main paper
6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
%prep
%autosetup -n pytwoway-0.3.21
%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-pytwoway -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.21-1
- Package Spec generated
|
