MSCMT: 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) <doi:10.1016/j.ecosta.2017.08.002>.

Version: 1.3.2
Depends: R (≥ 3.2.0)
Imports: stats, utils, parallel, lpSolve, ggplot2, lpSolveAPI, Rglpk, Rdpack
Suggests: Synth, DEoptim, rgenoud, DEoptimR, GenSA, GA, soma, cmaes, Rmalschains, NMOF, nloptr, hydroPSO, pso, LowRankQP, kernlab, reshape, knitr, rmarkdown
Published: 2018-02-19
Author: Martin Becker ORCID iD [aut, cre], Stefan Klößner [aut], Karline Soetaert [com], LAPACK authors [cph]
Maintainer: Martin Becker <martin.becker at>
License: GPL-2 | GPL-3 [expanded from: GPL]
Copyright: inst/COPYRIGHTS
MSCMT copyright details
NeedsCompilation: yes
Materials: NEWS
CRAN checks: MSCMT results


Reference manual: MSCMT.pdf
Vignettes: Checking and Improving Results of package Synth
SCM Using Time Series
Working with package MSCMT
Package source: MSCMT_1.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: MSCMT_1.2.0.tgz
OS X Mavericks binaries: r-oldrel: MSCMT_1.3.1.tgz
Old sources: MSCMT archive


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