qte (version 1.3.0)

spatt: spatt

Description

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

Usage

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

# NOT RUN {
##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)

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


# }