| Name | binsreg JSON |
| Version |
2.1.5
JSON |
| download |
| home_page | https://github.com/nppackages/binsreg |
| Summary | Implements binscatter methods, including partition selection, point estimation, pointwise and uniform inference methods, and graphical procedures. |
| upload_time | 2024-05-23 15:47:42 |
| maintainer | None |
| docs_url | None |
| author | Ricardo Masini |
| requires_python | >=3.6 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# BINSREG
Binscatter provides a flexible, yet parsimonious way of visualizing and summarizing large data sets and has been a popular methodology in applied microeconomics and other social sciences. The `binsreg` package provides tools for statistical analysis using the binscatter methods developed in Cattaneo, Crump, Farrell and Feng (2022). binsreg implements binscatter least squares regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binsqreg` implements binscatter quantile regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform conf idence band. `binsglm` implements binscatter generalized linear regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binstest` implements binscatter-based hypothesis testing procedures for parametric specifications of and shape restrictions on the unknown function of interest. `binspwc` implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. `binsregselect` implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, and clustering, among other features.
## Authors
Matias D. Cattaneo (<cattaneo@princeton.edu>)
Richard K. Crump (<richard.crump@ny.frb.org>)
Max H. Farrell (<maxhfarrell@ucsb.edu>)
Yingjie Feng (<fengyingjiepku@gmail.com>)
Ricardo Masini (<rmasini@princeton.edu>)
## Website
https://nppackages.github.io/binsreg/
## Major Upgrades
This package was first released in Winter 2019, and had one major upgrade in Summer 2021.
Summer 2021 new features include: (i) generalized linear models (logit, Probit, etc.) binscatter; (ii) quantile regression binscatter; (iii) new generic specification and shape restriction hypothesis testing function (now including Lp metrics); (iv) multi-group comparison of binscatter estimators; (v) generic point evaluation of covariate-adjusted binscatter; (vi) speed improvements and optimization. A complete list of upgrades can be found [here](https://nppackages.github.io/binsreg/binsreg_upgrades.txt).
## Installation
To install/update use pip
```
pip install binsreg
```
# Usage
```
from binsreg import binsregselect, binsreg, binsqreg, binsglm, binstest, binspwc
```
- Replication: [binsreg illustration](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_illustration.py), [simulated data](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_sim.csv).
## Dependencies
- numpy
- pandas
- scipy
- statsmodel
- plotnine
## References
For overviews and introductions, see [NP Packages website](https://nppackages.github.io/).
### Software and Implementation
- Cattaneo, Crump, Farrell and Feng (2023c): [Binscatter Regressions](https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_Stata.pdf).<br>
Working paper, prepared for Stata Journal.
### Technical and Methodological
- Cattaneo, Crump, Farrell and Feng (2023a): [On Binscatter](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_AER.pdf).<br>
Working paper.
- Cattaneo, Crump, Farrell and Feng (2023b): [Nonlinear Binscatter Methods](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf).<br>
Working paper.
Raw data
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"description": "# BINSREG\n\nBinscatter provides a flexible, yet parsimonious way of visualizing and summarizing large data sets and has been a popular methodology in applied microeconomics and other social sciences. The `binsreg` package provides tools for statistical analysis using the binscatter methods developed in Cattaneo, Crump, Farrell and Feng (2022). binsreg implements binscatter least squares regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binsqreg` implements binscatter quantile regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform conf idence band. `binsglm` implements binscatter generalized linear regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band. `binstest` implements binscatter-based hypothesis testing procedures for parametric specifications of and shape restrictions on the unknown function of interest. `binspwc` implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. `binsregselect` implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, and clustering, among other features.\n\n\n## Authors\n \nMatias D. Cattaneo (<cattaneo@princeton.edu>)\n\nRichard K. Crump (<richard.crump@ny.frb.org>)\n\nMax H. Farrell (<maxhfarrell@ucsb.edu>)\n\nYingjie Feng (<fengyingjiepku@gmail.com>)\n\nRicardo Masini (<rmasini@princeton.edu>)\n\n\n## Website\n\nhttps://nppackages.github.io/binsreg/\n\n## Major Upgrades\n\nThis package was first released in Winter 2019, and had one major upgrade in Summer 2021.\n\nSummer 2021 new features include: (i) generalized linear models (logit, Probit, etc.) binscatter; (ii) quantile regression binscatter; (iii) new generic specification and shape restriction hypothesis testing function (now including Lp metrics); (iv) multi-group comparison of binscatter estimators; (v) generic point evaluation of covariate-adjusted binscatter; (vi) speed improvements and optimization. A complete list of upgrades can be found [here](https://nppackages.github.io/binsreg/binsreg_upgrades.txt).\n\n\n## Installation\n\nTo install/update use pip\n```\npip install binsreg\n```\n\n# Usage\n```\nfrom binsreg import binsregselect, binsreg, binsqreg, binsglm, binstest, binspwc\n```\n\n- Replication: [binsreg illustration](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_illustration.py), [simulated data](https://github.com/nppackages/binsreg/blob/master/Python/binsreg_sim.csv).\n\n\n## Dependencies\n\n- numpy\n- pandas\n- scipy\n- statsmodel\n- plotnine\n\n## References\n\nFor overviews and introductions, see [NP Packages website](https://nppackages.github.io/).\n\n### Software and Implementation\n\n- Cattaneo, Crump, Farrell and Feng (2023c): [Binscatter Regressions](https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_Stata.pdf).<br>\nWorking paper, prepared for Stata Journal.\n\n### Technical and Methodological\n\n- Cattaneo, Crump, Farrell and Feng (2023a): [On Binscatter](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_AER.pdf).<br>\nWorking paper.\n\n- Cattaneo, Crump, Farrell and Feng (2023b): [Nonlinear Binscatter Methods](https://mdcattaneo.github.io/papers/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf).<br>\nWorking paper.\n",
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