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The goal of pgsc is to provide an estimation and testing framework for Powell’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.
You can install pgsc from github with:
{r gh-installation, eval = FALSE} devtools::install_github("philipbarrett/pgsc")
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')
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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