Jump to content

Synthetic control method

From Wikipedia, the free encyclopedia
This is an old revision of this page, as edited by RevelationDirect (talk | contribs) at 22:08, 29 January 2016 (removed Category:Control (social and political); added Category:Design of experiments using HotCat). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

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[1]

To construct our synthetic control unit, the vector of weights such that ≥ O, for j=2,...,J+1 and . Each W represents one particular weighted average of control units and therefore one potential synthetic control unit. The goal is to optimize the W* such that the resulting synthetic control unit best approximates the unit exposed to the intervention with respect to the outcome predictors and linear combinations of pre-intervention outcomes where

such that: , ..., and hold.

Then :

yields an estimator of in periods

The W* was solved by minimize:

, 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 pre-intervention period.

References

  1. ^ \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.