Pre-Test of Conditional Parallel Trends Assumption — conditional_did

An integrated moments test for the conditional parallel trends assumption holding in all pre-treatment time periods for all groups

conditional_did_pretest(
  yname,
  tname,
  idname = NULL,
  gname,
  xformla = NULL,
  data,
  panel = TRUE,
  allow_unbalanced_panel = FALSE,
  control_group = c("nevertreated", "notyettreated"),
  weightsname = NULL,
  alp = 0.05,
  bstrap = TRUE,
  cband = TRUE,
  biters = 1000,
  clustervars = NULL,
  est_method = "ipw",
  print_details = FALSE,
  pl = FALSE,
  cores = 1
)

Arguments

yname: The name of the outcome variable
tname: The name of the column containing the time periods
idname: The individual (cross-sectional unit) id name
gname: The name of the variable in data that contains the first period when a particular observation is treated. This should be a positive number for all observations in treated groups. It defines which "group" a unit belongs to. It should be 0 for units in the untreated group.
xformla: A formula for the covariates to include in the model. It should be of the form ~ X1 + X2. Default is NULL which is equivalent to xformla=~1. This is used to create a matrix of covariates which is then passed to the 2x2 DID estimator chosen in est_method.
data: The name of the data.frame that contains the data
panel: Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When is using a panel dataset, the variable idname must be set. When panel=FALSE, the data is treated as repeated cross sections.
allow_unbalanced_panel: Whether or not function should "balance" the panel with respect to time and id. The default values if FALSE which means that att_gt() will drop all units where data is not observed in all periods. The advantage of this is that the computations are faster (sometimes substantially).
control_group: Which units to use the control group. The default is "nevertreated" which sets the control group to be the group of units that never participate in the treatment. This group does not change across groups or time periods. The other option is to set group="notyettreated". In this case, the control group is set to the group of units that have not yet participated in the treatment in that time period. This includes all never treated units, but it includes additional units that eventually participate in the treatment, but have not participated yet.
weightsname: The name of the column containing the sampling weights. If not set, all observations have same weight.
alp: the significance level, default is 0.05
bstrap: Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set bstrap=TRUE. Default is TRUE (in addition, cband is also by default TRUE indicating that uniform confidence bands will be returned. If bstrap is FALSE, then analytical standard errors are reported.
cband: Boolean for whether or not to compute a uniform confidence band that covers all of the group-time average treatment effects with fixed probability 1-alp. In order to compute uniform confidence bands, bstrap must also be set to TRUE. The default is TRUE.
biters: The number of bootstrap iterations to use. The default is 1000, and this is only applicable if bstrap=TRUE.
clustervars: A vector of variables names to cluster on. At most, there can be two variables (otherwise will throw an error) and one of these must be the same as idname which allows for clustering at the individual level. By default, we cluster at individual level (when bstrap=TRUE).
est_method: the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust approach in the DRDID package. Other built-in methods include "ipw" for inverse probability weighting and "reg" for first step regression estimators. The user can also pass their own function for estimating group time average treatment effects. This should be a function f(Y1,Y0,treat,covariates) where Y1 is an n x 1 vector of outcomes in the post-treatment outcomes, Y0 is an n x 1 vector of pre-treatment outcomes, treat is a vector indicating whether or not an individual participates in the treatment, and covariates is an n x k matrix of covariates. The function should return a list that includes ATT (an estimated average treatment effect), and inf.func (an n x 1 influence function). The function can return other things as well, but these are the only two that are required. est_method is only used if covariates are included.
print_details: Whether or not to show details/progress of computations. Default is FALSE.
pl: Whether or not to use parallel processing
cores: The number of cores to use for parallel processing

Value

an MP.TEST object

References

Callaway, Brantly and Sant'Anna, Pedro H. C. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment." Working Paper https://arxiv.org/abs/1803.09015v2 (2018).

Examples

if (FALSE) { # \dontrun{
data(mpdta)
pre.test <- conditional_did_pretest(yname="lemp",
                                    tname="year",
                                    idname="countyreal",
                                    gname="first.treat",
                                    xformla=~lpop,
                                    data=mpdta)
summary(pre.test)
} # }