synth_method {tidysynth}

synth_method

Description

AUX Function: Original synthetic control method proposed by (Abadie et al. 2003, 2010, 2015) and implemented in synth package. Method has been commandeered for internal use here.

Usage

synth_method(
  treatment_unit_covariates = NULL,
  control_units_covariates = NULL,
  control_units_outcome = NULL,
  treatment_unit_outcome = NULL,
  custom.v = NULL,
  optimxmethod = c("Nelder-Mead", "BFGS"),
  genoud = FALSE,
  Margin.ipop = 5e-04,
  Sigf.ipop = 5,
  Bound.ipop = 10,
  verbose = FALSE,
  ...
)

Arguments

treatment_unit_covariates

matrix of treated predictor data

control_units_covariates

matrix of controls' predictor data.

control_units_outcome

matrix of controls' outcome data for the pre-treatment periods over which MSPE is to be minimized.

treatment_unit_outcome

matrix of treated outcome data for the pre-treatment periods over which MSPE is to be minimized.

custom.v

vector of weights for predictors supplied by the user. uses synth to bypass optimization for solution.V. See details.

optimxmethod

string vector that specifies the optimization algorithms to be used. Permissible values are all optimization algorithms that are currently implemented in the optimx function (see this function for details). This list currently includes c("Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B', 'nlm', 'nlminb', 'spg', and 'ucminf"). If multiple algorithms are specified, synth will run the optimization with all chosen algorithms and then return the result for the best performing method. Default is c("Nelder-Mead", "BFGS"). As an additional possibility, the user can also specify 'All' which means that synth will run the results over all algorithms in optimx.

genoud

Logical flag. If true, synth embarks on a two step optimization. In the first step, genoud, an optimization function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to solve difficult optimization problems, is used to obtain a solution. See genoud for details. In the second step, the genoud results are passed to the optimization algorithm(s) chosen in optimxmethod for a local optimization within the neighborhood of the genoud solution. This two step optimization procedure will require much more computing time, but may yield lower loss in cases where the search space is highly irregular.

Margin.ipop

setting for ipop optimization routine: how close we get to the constrains (see ipop for details)

Sigf.ipop

setting for ipop optimization routine: Precision (default: 7 significant figures (see ipop for details)

Bound.ipop

setting for ipop optimization routine: Clipping bound for the variables (see ipop for details)

verbose

Logical flag. If TRUE then intermediate results will be shown.

...

Additional arguments to be passed to optimx and or genoud to adjust optimization.

Details

Synth works as the main engine of the tidysynth package. More on the method and estimation procedures can be found in (Abadie et al. 2010).

As proposed in Abadie and Gardeazabal (2003) and Abadie, Diamond, Hainmueller (2010), the synth function routinely searches for the set of weights that generate the best fitting convex combination of the control units. In other words, the predictor weight matrix V is chosen among all positive definite diagonal matrices such that MSPE is minimized for the pre-intervention period. Instead of using this data-driven procedures to search for the best fitting synthetic control group, the user may supply his own vector of V weights, based on his subjective assessment of the predictive power of the variables in treatment_unit_covariates and control_units_covariates. In this case, the vector of V weights for each variable should be supplied via the custom.V option in synth and the optimization over the V matrices is bypassed.

Value

solution.v = vector of predictor weights; solution.w = vector of weights across the controls; loss.v = MSPE from optimization over v and w weights; loss.w = Loss from optimization over w weights; custom.v =if this argument was specified in the call to synth, this outputs the weight vector specified; rgV.optim = Results from optimx() minimization. Could be used for diagnostics.