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.
Changes in version 1.1.0 (2016-11-29)
newly supported outer optimizer "none" to implant 'artificial' solutions (fixed w and optionally fixed v).
many new outer optimizers are now supported, more documentation will follow.
function compare() for comparison of estimation results, including corresponding updates for print.mscmt and ggplot.mscmt.
names of agg.fns (component of mscmt results) are now set automatically.
user-defined agg.fns are now automatically exported to the cluster.
changed default optimizer to DEoptC.
predictor weights are not printed any more as verbose output in estimations.
some packages were moved from "Imports" to "Suggests".
number of calls to inner optimizer is now counted for benchmarking purposes.
package startup message added.
objective function weights alpha/beta/gamma are now treated correctly.
calls to the lp solver are now more robust.
some typos in vignettes have been corrected.
all data had to be sorted chronologically, this is now optional.
improveSynth reported wrong 'True' W*(V) if W*(V) was not optimal.
Changes in version 1.0.0 (2016-07-25)