# pgsc

The goal of pgsc is to provide an estimation and testing framework for Powellâ€™s Generalized Synthetic Control method. This provides consistent estimates in the presence of unobserved spatially-correlated factors in a panel. Please see the vignette for further details and an extended example.

## Installation

You can install pgsc from github with:

`{r gh-installation, eval = FALSE} devtools::install_github("philipbarrett/pgsc")`

## Example

This is a basic example which provides estimation and testing for a dataset with omitted variables that cannot be addressed by time and unit fixed effects. Please see the vignette for further details on this example and other options.

`{r example} #' data("pgsc.dta") #' library(plm) #' pan <- plm( y ~ D1 + D2 + X1 + X2 + X3, pgsc.dta, effect = 'twoways', index = c('n','t')) #' summary(pan) #' # Failure of panel estimation: the true coefficients on D1, D2 are c(1,2), #' # which are not recovered due to omitted variables which are spatially #' # correlated. The "twoway" (time/unit) fixed effects cannot pick this up. #' sol <- pgsc(pgsc.dta, dep.var = 'y', indep.var = c('D1','D2'), #' b.init = c(0,0), method='twostep.indiv' ) #' summary(sol) #' # The unrestricted estimation. #' 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='twostep.indiv', g.i=g.i, g.i.grad=g.i.grad ) #' # Restricted estimation under the hypothesis that b[1]=0 #' summary(sol.r) #' wald <- pgsc.wald.test( pgsc.dta, 'y', indep.var = c('D1','D2'), sol.r ) #' summary(wald) #' plot(wald) #' # Testing the hypothesized restriction. It is comfortably rejected.`

You can access the vignette with

`{r example} #' browseVignettes('pgsc')`