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 )
The name of the outcome variable
The name of the column containing the time periods
The individual (cross-sectional unit) id name
The name of the variable in
A formula for the covariates to include in the
model. It should be of the form
The name of the data.frame that contains the data
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
Whether or not function should
"balance" the panel with respect to time and id. The default
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
The name of the column containing the sampling weights. If not set, all observations have same weight.
the significance level, default is 0.05
Boolean for whether or not to compute standard errors using
the multiplier bootstrap. If standard errors are clustered, then one
Boolean for whether or not to compute a uniform confidence
band that covers all of the group-time average treatment effects
with fixed probability
The number of bootstrap iterations to use. The default is 1000,
and this is only applicable if
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
the method to compute group-time average treatment effects. The default is "dr" which uses the doubly robust
approach in the
Whether or not to show details/progress of computations.
Whether or not to use parallel processing (not implemented yet)
The number of cores to use for parallel processing (not implemented yet)
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).