Synthetic control method: Difference between revisions

Revision as of 22:08, 29 January 2016

Revision as of 15:07, 1 April 2016

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Abadie et al. (2010) motivate the '''synthetic control method''' with a model that generalizes the [[difference-in-differences]] (fixed-effects) model commonly applied in the empirical social science literature by allowing the effect of unobserved confounding characteristics to vary over time. An attractive feature of the synthetic control method is that it guards against extrapolation outside the [[convex hull]] of the data because weights from all control units can be chosen to be positive and sum to one.

==Synthetic Control Method models<ref>\Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program."Journal of the American Statistical Association, 105(490), 493{505.</ref>==

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The W* was solved by minimize:

<math> \left\| \mathbf{X_1-X_0W} \right\|_V = \sqrt{(X_1-X_0W)'V(X_1-X_0W)}</math>, where ''''V'''' is defined as (k×k) symmetric and positive semidefinite matrix. V* is chosen among all positive definite and diagnal matrices such that the mean square prediction error (MSPE) of the outcome variable is minimized over the pre-intervention period.

==References==

{{Reflist}}

[[Category:Design of experiments]]