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https://github.com/nppackages/scpi
Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
https://github.com/nppackages/scpi
prediction-intervals python r stata synthetic-control
Last synced: 3 months ago
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Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.
- Host: GitHub
- URL: https://github.com/nppackages/scpi
- Owner: nppackages
- Created: 2021-07-29T14:33:43.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-14T11:22:45.000Z (6 months ago)
- Last Synced: 2024-03-14T16:04:55.104Z (3 months ago)
- Topics: prediction-intervals, python, r, stata, synthetic-control
- Language: R
- Homepage:
- Size: 10.2 MB
- Stars: 27
- Watchers: 2
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Lists
- awesome-marketing-machine-learning - scpi
- awesome-stars - nppackages/scpi - Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption. (R)
README
# SCPI
The `scpi` package provides Python, R and Stata implementations of estimation and inference procedures for synthetic control methods.
This work was supported by the National Science Foundation through grants [SES-1947805](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1947805), [SES-2019432](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019432), and [SES-2241575](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241575), and by the National Institutes of Health through grant [R01 GM072611-16](https://reporter.nih.gov/project-details/10093056).
## Queries and Requests
Please email: [[email protected]](mailto:[email protected])
## Python Implementation
To install/update in Python type:
```
pip install scpi_pkg
```
- Help: [PyPI repository](https://pypi.org/project/scpi_pkg/).
- Replication: [py-script](Python/scpi_illustration.py), [plot illustration](Python/scpi_illustration_plot.py), [data](Python/scpi_germany.csv).
- Illustration Staggered Adoption: [py-script](Python/scpi_illustration-multi.py), [plot illustration](Python/scpi_illustration_plot-multi.py).
## R Implementation
To install/update in R from CRAN type:
```
install.packages('scpi')
````
- Help: [R Manual](https://cran.r-project.org/web/packages/scpi/scpi.pdf), [CRAN repository](https://cran.r-project.org/package=scpi).
- Replication: [R-script](R/scpi_illustration.R), [plot illustration](R/scpi_illustration_plot.R), [data](R/scpi_germany.csv).
- Illustration Staggered Adoption: [R-script](R/scpi_illustration-multi.R), [plot illustration](R/scpi_illustration_plot-multi.R).
## Stata Implementation
The Stata implementation relies on Python, which needs to be available in the system.
### How to install Python
There are at least two ways to install Python:
1. Download and install Python directly from [https://realpython.com/installing-python/](https://realpython.com/installing-python/).
2. Download and install Anaconda for [Windows](https://docs.anaconda.com/anaconda/install/windows/), [macOS](https://docs.anaconda.com/anaconda/install/mac-os/), or [Linux](https://docs.anaconda.com/anaconda/install/linux/).
After Python is installed, please run the following two commands via the Python command line:
```
pip install luddite
pip install scpi_pkg
```
### How to link Stata and Python
Stata (16.0 or newer) and Python (>=3.8) can be linked following the [official tutorial](https://blog.stata.com/2020/08/18/stata-python-integration-part-1-setting-up-stata-to-use-python/) on the Stata blog.
### To install/update in Stata type:
```
net install grc1leg, from("http://www.stata.com/users/vwiggins/") replace force
net install scpi, from(https://raw.githubusercontent.com/nppackages/scpi/master/stata) replace force
```
- Help: [scdata](stata/scdata.pdf), [scest](/stata/scest.pdf), [scpi](stata/scpi.pdf), [scplot](stata/scplot.pdf).
- Replication files: [do-file](stata/scpi_illustration.do), [plot illustration](stata/scpi_illustration_plot.do), [data](stata/scpi_germany.dta).
- Illustration Staggered Adoption: [do-file](stata/scpi_illustration-multi.do), [plot illustration](stata/scpi_illustration_plot-multi.do).
## References
### Software and Implementation
- Cattaneo, Feng, Palomba and Titiunik (2024): [scpi: Uncertainty Quantification for Synthetic Control Methods](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2024_JSS.pdf).
_Journal of Statistical Software_, forthcoming.
### Technical and Methodological
- Cattaneo, Feng, Palomba and Titiunik (2023): [Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2023_wp.pdf).
Working paper.
[Supplemental](https://nppackages.github.io/references/Cattaneo-Feng-Palomba-Titiunik_2023_wp--Supplement.pdf)
- Cattaneo, Feng and Titiunik (2021): [Prediction Intervals for Synthetic Control Methods](https://nppackages.github.io/references/Cattaneo-Feng-Titiunik_2021_JASA.pdf).
_Journal of the American Statistical Association_ 116(536): 1865-1880.
[Supplemental](https://nppackages.github.io/references/Cattaneo-Feng-Titiunik_2021_JASA--Supplement.pdf)