%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.aliyun.com/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? `_" 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 `_ 2. homoskedastic bias correction as in `Andrews, et al. `_ 3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten `_ 4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa `_ 5. group correlated random effect as presented in the main paper 6. fixed-point revealed preference estimator as in `Sorkin `_ 7. non-parametric sorting estimator as in `Borovičková and Shimer `_ 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 `_. The online documentation is hosted `here `_. The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `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? `_" 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 `_ 2. homoskedastic bias correction as in `Andrews, et al. `_ 3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten `_ 4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa `_ 5. group correlated random effect as presented in the main paper 6. fixed-point revealed preference estimator as in `Sorkin `_ 7. non-parametric sorting estimator as in `Borovičková and Shimer `_ 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 `_. The online documentation is hosted `here `_. The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `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? `_" 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 `_ 2. homoskedastic bias correction as in `Andrews, et al. `_ 3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten `_ 4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa `_ 5. group correlated random effect as presented in the main paper 6. fixed-point revealed preference estimator as in `Sorkin `_ 7. non-parametric sorting estimator as in `Borovičková and Shimer `_ 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 `_. The online documentation is hosted `here `_. The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `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 * Thu Jun 08 2023 Python_Bot - 0.3.21-1 - Package Spec generated