pgsc {pgsc}

Wrapper function for GSC estimation

Description

Wrapper function for GSC estimation

Usage

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,
  ...)

Arguments

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

Details

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

Value

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)

Examples

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)