panel.qtet: panel.qtet

Description

Deprecated. Use panel_qtt instead. panel_qtt supports staggered treatment adoption and exposes the pre_copula option for controlling how the copula is transferred across pre-treatment periods.

panel.qtet computes the Quantile Treatment Effect on the Treated (QTET) using the method of Callaway and Li (2015). This method should be used when the researcher wants to invoke a Difference in Differences assumption to identify the QTET. Relative to the other Difference in Differences methods available in the qte package, this method's assumptions are more intuitively similar to the identifying assumptions used in identifying the Average Treatment Effect on the Treated (ATT).

Additionally, this method can accommodate covariates in a more flexible way than the other Difference in Differences methods available. In order to accommodate covariates, the user should specify a vector x of covariate names. The user also may specify a method for estimating the propensity score. The default is logit.

panel.qtet can only be used in some situations, however. The method requires three periods of panel data where individuals are not treated until the last period. The data should be formatted as a panel; the names of columns containing time periods and ids for each cross sectional unit need to be passed to the method.

Usage

panel.qtet(
  formla,
  xformla = NULL,
  t,
  tmin1,
  tmin2,
  tname,
  data,
  idname,
  probs = seq(0.05, 0.95, 0.05),
  iters = 100,
  alp = 0.05,
  method = c("qr", "pscore"),
  se = TRUE,
  retEachIter = FALSE,
  pl = FALSE,
  cores = NULL,
  biters = NULL,
  cl = NULL
)

Value

QTE object

Arguments

formla: The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated
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. Treated individuals should be treated in this time period and untreated individuals should not be treated. The code attempts to enforce this condition, but it is good try to handle this outside the panel.qtet method.
tmin1: The 2nd time period in the sample. This should be a pre-treatment period for all individuals in the sample.
tmin2: The 1st time period in the sample. This should be a pre-treatment period for all individuals in the sample.
tname: The name of the column containing the time periods
data: A data.frame containing all the variables 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 including covariates, should either be "QR" for quantile regression or "pscore" for propensity score
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 (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory.
pl: Whether or not to compute standard errors in parallel
cores: Number of cores to use if computing in parallel
biters: Number of bootstrap iterations; alias for iters matching the did/ptetools naming convention. If both are supplied, biters takes precedence.
cl: Number of cores for parallel bootstrap; alias for pl/cores. cl = 1 (default) runs sequentially; cl > 1 enables parallelism.

References

Callaway, Brantly and Tong Li. ``Quantile Treatment Effects in Difference in Differences Models with Panel Data.'' Working Paper, 2019.

Examples

Run this code

# See ?panel_qtt for the modern replacement.
if (FALSE) {
data(lalonde)

## Run the panel.qtet method on the experimental data with no covariates
pq1 <- panel.qtet(re ~ treat,
  t = 1978, tmin1 = 1975, tmin2 = 1974, tname = "year",
  data = lalonde.exp.panel, idname = "id", se = FALSE,
  probs = seq(0.05, 0.95, 0.05)
)
summary(pq1)

## Run the panel.qtet method on the observational data with no covariates
pq2 <- panel.qtet(re ~ treat,
  t = 1978, tmin1 = 1975, tmin2 = 1974, tname = "year",
  data = lalonde.psid.panel, idname = "id", se = FALSE,
  probs = seq(0.05, 0.95, 0.05)
)
summary(pq2)

## Run the panel.qtet method on the observational data conditioning on
## age, education, black, hispanic, married, and nodegree.
## The propensity score will be estimated using the default logit method.
pq3 <- panel.qtet(re ~ treat,
  t = 1978, tmin1 = 1975, tmin2 = 1974, tname = "year",
  xformla = ~ age + I(age^2) + education + black + hispanic + married + nodegree,
  data = lalonde.psid.panel, idname = "id", se = FALSE, method = "pscore",
  probs = seq(0.05, 0.95, 0.05)
)
summary(pq3)

pq4 <- panel.qtet(re ~ treat,
  t = 1978, tmin1 = 1975, tmin2 = 1974, tname = "year",
  xformla = ~ age + I(age^2) + education + black + hispanic + married + nodegree,
  data = lalonde.psid.panel, idname = "id", se = FALSE, method = "qr",
  probs = seq(0.05, 0.95, 0.05)
)
summary(pq4)
}