ddid2: ddid2

In qte: Quantile Treatment Effects

ddid2

Description

ddid2 computes the Quantile Treatment Effect on the Treated (QTET) using the method of Callaway, Li, and Oka (2015).

Usage

ddid2(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tname,
  data,
  panel = TRUE,
  dropalwaystreated = TRUE,
  idname = NULL,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = "logit",
  se = TRUE,
  retEachIter = FALSE,
  seedvec = NULL,
  pl = FALSE,
  cores = NULL
)

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
panel	Boolean indicating whether the data is panel or repeated cross sections
dropalwaystreated	How to handle always treated observations in panel data case (not currently used)
idname	The individual (cross-sectional unit) id name
probs	A vector of values between 0 and 1 to compute the QTET at
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
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

Callaway, Brantly, Tong Li, and Tatsushi Oka. “Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with Only Two Time Periods.” Working Paper, 2015.

Examples

##load the data
data(lalonde)

## Run the ddid2 method on the observational data with no covariates
d1 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(d1)

## Run the ddid2 method on the observational data with covariates
d2 <- ddid2(re ~ treat, t=1978, tmin1=1975, tname="year",
 data=lalonde.psid.panel, idname="id", se=FALSE,
 xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
 probs=seq(0.05, 0.95, 0.05))
summary(d2)

qte documentation built on Sept. 1, 2022, 5:05 p.m.