ddid2 function

ddid2

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

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

Returns

QTE object

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)

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.