spatt — spatt • qte

spatt computes the Average Treatment Effect on the Treated (ATT) using the method of Abadie (2005)

spatt(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  w = NULL,
  panel = FALSE,
  idname = NULL,
  iters = 100,
  alp = 0.05,
  method = "logit",
  plot = FALSE,
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = 2
)

Arguments

formla: The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary)
xformla: A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater.
t: The 3rd time period in the sample (this is the name of the column)
tmin1: The 2nd time period in the sample (this is the name of the column)
tname: The name of the column containing the time periods
data: The name of the data.frame that contains the data
w: an additional vector of sampling weights
panel: Boolean indicating whether the data is panel or repeated cross sections
idname: The individual (cross-sectional unit) id name
iters: The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE
alp: The significance level used for constructing bootstrap confidence intervals
method: The method for estimating the propensity score when covariates are included
plot: Boolean whether or not the estimated QTET should be plotted
se: Boolean whether or not to compute standard errors
retEachIter: Boolean whether or not to return list of results from each iteration of the bootstrap procedure
seedvec: Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors.
pl: boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows.
cores: the number of cores to use if bootstrap standard errors are computed in parallel

Value

QTE object

References

Abadie (2005)

Examples

##load the data
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
att1 <- spatt(re ~ treat, t=1978, tmin1=1975, tname="year",
 x=NULL, data=lalonde.psid.panel, idname="id", se=FALSE)
summary(att1)
#> Average Treatment Effect:	2326.51

## Run the panel.qtet method on the observational data with no covariates