Multivariate Synthetic Control Method Using Time Series

Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) .


Reference manual

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install.packages("MSCMT")

1.4.4 by Martin Becker, 4 months ago


https://github.com/mabe0033/MSCMT


Report a bug at https://github.com/mabe0033/MSCMT/issues


Browse source code at https://github.com/cran/MSCMT


Authors: Martin Becker [aut, cre] (ORCID: , Stefan Klößner [aut] , Karline Soetaert [com] , Jack Dongarra [cph] , R.J. Hanson [cph] , K.H. Haskell [cph] , Cleve Moler [cph] , LAPACK authors [cph]


Documentation:   PDF Manual  


GPL license


Imports stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk, Rdpack, rlang

Suggests Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes, Rmalschains, NMOF, nloptr, pso, LowRankQP, kernlab, reshape, knitr, rmarkdown


See at CRAN