Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2021) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching is done, both short-term and long-term average treatment effects for the treated can be estimated with standard errors. The package also offers a visualization technique that allows researchers to assess the quality of matches by examining the resulting covariate balance.
Package details |
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Author | In Song Kim [aut, cre], Adam Rauh [aut], Erik Wang [aut], Kosuke Imai [aut] |
Maintainer | In Song Kim <insong@mit.edu> |
License | GPL (>= 3) |
Version | 2.0.1 |
Package repository | View on CRAN |
Installation |
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