pgsc: Wrapper function for GSC estimation
In pgsc: Computes Powell's Generalized Synthetic Control Estimator

Description Usage Arguments Details Value Examples

Wrapper function for GSC estimation

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pgsc(dta, dep.var, indep.var, b.init, method, sol.it = NULL,
  wt.init = NULL, print.level = 0, g.i = NULL, g.i.grad = NULL,
  ...)

`dta`	A data frame
`dep.var`	A string defining the dependent variable
`indep.var`	A vector of strings defining the independent (treatment) variables
`b.init`	An initial value for the treatment variable coefficients. Must have same length as 'indep.var'
`method`	The GSC iteration method to be used. Must be one of: `onestep`: "Plain" GSC solution, without weights `twostep.aggte`: Observations weighted by unit MSEs from the one-step solution. `twostep.indiv`: Observations weighted by unit MSEs from individual, unit-by-unit unweighted solutions.
`sol.it`	The first step solution used in the two-step methods. If omitted, a new one-step solution is computed.
`wt.init`	An initial value for the weighting matrix
`print.level`	The level of detail provided in the printed output
`g.i`	A function defining a restriction on the parameters. Used in hypothesis testing.
`g.i.grad`	The gradient of `g.i`.
`...`	Other arguments to be passed to the optimization

See the vignette "Using pgsc" for an extended example.

Returns the point estimate of the model as a gsc object, a list with entries:

b: The point estimate of the coefficients on the dependent variables
diff: The difference between successive iterations
err: The maximum error on the within-iteration optimization problems
it: Number of iterations require to solve
sig.i: The unit-specific MSEs from the solution
W: The "full" weighting matrix for counterfactuals, containing own-unit weights (all zero) and unit-N weights
wt: The "minimal" weighting matrix, omitting own-unit weights and weights on unit N (which can be computed as one-minus-rowsum)

data("pgsc.dta")
sol <- pgsc(pgsc.dta, dep.var = 'y', indep.var = c('D1','D2'), 
b.init = c(0,0), method='onestep' )
summary(sol)
g.i <- function(b) b[1] ; g.i.grad <- function(b) c(1,0)
sol.r <- pgsc(pgsc.dta, dep.var = 'y', indep.var = c('D1','D2'), 
b.init = sol$b, method='onestep', g.i=g.i, g.i.grad=g.i.grad )
summary(sol.r)