scpi-package {scpi}

scpi: A Package to Compute Synthetic Control Prediction Intervals With Multiple Treated Units and Staggered Adoption

Description

The package implements estimation, inference procedures, and produces plots for Synthetic Control (SC) methods using least squares, lasso, ridge, or simplex-type constraints. Uncertainty is quantified using prediction intervals according to Cattaneo, Feng, and Titiunik (2021).

Included functions are: scdata and scdataMulti for data preparation, scest for point estimation, scpi for inference procedures, and scplot and scplotMulti for plots.

print() and summary() methods are available for scest and scpi.

Companion Stata and Python packages are described in Cattaneo, Feng, Palomba, and Titiunik (2022).

Related Stata, R, and Python packages useful for inference in SC designs are described in the following website:

https://nppackages.github.io/scpi/

For an introduction to synthetic control methods, see Abadie (2021) and references therein.

Author(s)

Matias Cattaneo, Princeton University. [e-mail].

Yingjie Feng, Tsinghua University. [e-mail].

Filippo Palomba, Princeton University (maintainer). [e-mail].

Rocio Titiunik, Princeton University. [e-mail].

References

• Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.

• Cattaneo, M. D., Feng, Y., and Titiunik, R. (2021). Prediction intervals for synthetic control methods. Journal of the American Statistical Association, 116(536), 1865-1880.

• Cattaneo, M. D., Feng, Y., Palomba F., and Titiunik, R. (2022). scpi: Uncertainty Quantification for Synthetic Control Methods, arXiv:2202.05984.

• Cattaneo, M. D., Feng, Y., Palomba F., and Titiunik, R. (2022). Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption, arXiv:2210.05026.

See Also

Useful links:

• https://nppackages.github.io/scpi/