.. _replication-ferman: Demeaned SC (Ferman & Pinto 2021) -- via TSSC's MSCa variant ============================================================ :Estimator: :doc:`../tssc` -- :class:`mlsynth.TSSC` (the ``MSCa`` variant) :Source: Ferman, B. and Pinto, C. (2021), *"Synthetic controls with imperfect pretreatment fit,"* Quantitative Economics 12:1197-1221. :Replication type: golden cross-validation against the authors' own R code, executed live via ``Rscript`` on every run. :Status: verified -- mlsynth's ``MSCa`` reproduces the authors' demeaned-SC quadratic program value-for-value on the identified Basque panel. What the paper proposes ----------------------- Ferman and Pinto (2021) study synthetic control when the pre-treatment fit is imperfect. They show that SC and related estimators are generally biased in that regime, and propose a *demeaned* synthetic control -- donor weights on the simplex (non-negative, summing to one) together with a free intercept -- as a bias-and-variance improvement over difference-in-differences, along with a specification test for it. Why this uses TSSC (and why that is incidental) ----------------------------------------------- mlsynth has no estimator called "demeaned SC," and it does not need one: the demeaned SC is exactly the ``MSCa`` variant of :doc:`../tssc` -- the Two-Step SC's simplex-plus-intercept model. This case therefore validates the *estimator*, not TSSC's variant-selection machinery; it reaches into the ``MSCa`` fit and ignores the Step-1 test. Adding a separate "demeaned SC" estimator or bolting an intercept onto ``VanillaSC`` would be redundant -- ``MSCa`` already is it. The reference is the authors' own code -------------------------------------- The reference is not transcribed numbers: it is the authors' ``_aux.R :: synth_control_est_demean`` (a ``quadprog`` QP), reproduced verbatim in ``benchmarks/reference/ferman_demeaned_basque/reference.R`` and **run live via Rscript on the same shipped panel every time the benchmark runs**. The case ``BenchmarkSkipped``s when ``Rscript`` or ``quadprog`` is absent, so a missing R toolchain never turns the suite red. Cross-validation -- the identified case (1975) ---------------------------------------------- The panel is the Basque Country / ETA terrorism study (Abadie & Gardeazabal 2003), ``basedata/basque_data.csv``, with treatment in 1975. That year is chosen deliberately: it is the *identified* regime -- twenty pre-treatment periods (1955-1974) against sixteen donors, so :math:`C < n` and the demeaned-SC weights are unique. mlsynth's ``MSCa`` QP and the authors' ``quadprog`` QP then agree value-for-value: .. list-table:: :header-rows: 1 :widths: 46 27 27 * - Quantity - MSCa (mlsynth) - demeaned SC (R, live) * - Cataluña weight - 0.5605 - 0.5605 * - Rioja weight - 0.2792 - 0.2792 * - Asturias weight - 0.1064 - 0.1064 * - Madrid weight - 0.0539 - 0.0539 * - free intercept - 0.5922 - 0.5922 * - ATT (GDP per capita) - :math:`-0.797` - :math:`-0.797` Donor-weight L1 distance :math:`\approx 10^{-4}`, the intercept agrees to :math:`\approx 10^{-5}`, and the ATT to :math:`\approx 4\times10^{-4}` -- the residual is just the two QP solvers' tolerances. A note on identification (why 1975, not 1970) --------------------------------------------- The paper is about *imperfect* pre-treatment fit, so it is worth being explicit about when the demeaned-SC estimate is even well-defined. With a 1970 cutoff the Basque panel is rank-deficient -- sixteen donors against fifteen pre-treatment years, :math:`C > n`. The demeaned-SC weights are then *not unique*: many weight vectors fit the pre-period equally well, and mlsynth's QP and the authors' own ``constrOptim`` fallback legitimately land on different minimisers (a Rioja / Cataluña corner versus a Murcia-heavy corner), giving different ATTs. That is not a disagreement between implementations -- it is non-identification of the estimand. Moving the cutoff to 1975 makes :math:`C < n`, the solution unique, and the two codes coincide. The benchmark pins the identified case; the non-identified contrast is documented here as the cautionary half of the story. Verification ------------ The check is committed as the ``ferman_demeaned_basque`` benchmark case (`benchmarks/cases/ferman_demeaned_basque.py `__), which runs :class:`~mlsynth.TSSC` and the authors' R demeaned SC live, side by side, and asserts the donor weights, the free intercept, and the ATT agree to solver tolerance. It appears on the :doc:`../validation` dashboard under TSSC. The fast offline unit test is ``mlsynth/tests/test_ferman_replication.py``.