Hi,
I recently read about the synthetic control method in overview
articles and didn't look at more than a summary before. I tried to get
a quick overview to see how difficult this would be.
It might not be a small project because it doesn't use anything for
which we would have already a larger amount of infrastructure. Also,
the nested optimization might be a bit tricky, according to the
authors of MSCMT, e.g.
https://cran.r-project.org/web/packages/MSCMT/vignettes/CheckingSynth.html
One problem might be that the weights are estimated under
non-negativity constraints where some of the constraints are likely to
be binding. I haven't looked at the details, but the only constrained
optimizer we currently use is for discrete models fit_regularized
which uses slsqp for non-negativity constraints. Maybe scipy has now
something else that would be appropriate.
I will try to look again next week to see if I get a better overview,
or at least learn enough to ask some questions.
If anyone wants to work on this, this would be a good topic.
Related: some versions for matching estimators or propensity score
matching are outside of statsmodels
https://github.com/statsmodels/statsmodels/issues/2871 (maybe not up
to date)
Josef
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