Skip to main content

Estimate two way fixed effect labor models

Project description

pytwoway

https://badge.fury.io/py/pytwoway.svg https://travis-ci.com/tlamadon/pytwoway.svg?branch=master

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 correction as in KSS (TBD)

  4. a group fixed estimator as in BLM

  5. a group correlated random effect as presented in the main paper

If you want to give it a try, you can start the example notebook here: binder. This starts a fully interactive notebook with a simple example that generates data and runs the estimators.

The code is relatively efficient. Solving large sparse linear models relies on using pyamg. This is the code we used to estimate the different decompositions on the US data.

The package provides a python interface as well as an intuitive command line 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.

Quick Start

To install from pip, run:

pip install pytwoway

To run using the command line interface:

pytw --my-config config.txt --fe --cre

Example config.txt:

data = file.csv
filetype = csv
col_dict = "{'fid': 'your_firmid_col', 'wid': 'your_workerid_col', 'year': 'your_year_col', 'comp': 'your_compensation_col'}"

Citation

Please use following citation to cite pytwoway in academic publications:

Bibtex entry:

@techreport{bhlmms2020,
  title={How Much Should We Trust Estimates of Firm Effects and Worker Sorting?},
  author={Bonhomme, St{\'e}phane and Holzheu, Kerstin and Lamadon, Thibaut and Manresa, Elena and Mogstad, Magne and Setzler, Bradley},
  year={2020},
  institution={National Bureau of Economic Research}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytwoway-0.1.2.tar.gz (33.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pytwoway-0.1.2-py3-none-any.whl (47.7 kB view details)

Uploaded Python 3

File details

Details for the file pytwoway-0.1.2.tar.gz.

File metadata

  • Download URL: pytwoway-0.1.2.tar.gz
  • Upload date:
  • Size: 33.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.24.0 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for pytwoway-0.1.2.tar.gz
Algorithm Hash digest
SHA256 2846231db8a94086131b7c4faf115cd0ee41724cf3c8ddde4901f9837ac670ea
MD5 f958c9eef591e71fa2f11f7f90893e9d
BLAKE2b-256 116bd60ed9f34356077349c98efe82829c68f3282d432aaed60f3f53b3ae4d24

See more details on using hashes here.

File details

Details for the file pytwoway-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: pytwoway-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 47.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.6.1 requests/2.24.0 setuptools/51.0.0.post20201207 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.7.9

File hashes

Hashes for pytwoway-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2bb6bf1fe53b4951e70bc2ad3a3830eea3b59feaccef77a59c5417ff20fc9697
MD5 bbd0ed9549a7fc32d63c18b7c225bf44
BLAKE2b-256 736425c49880e8076324c8918e9a5a5dc232724b21972a1f171c1bd83da324a0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page