An internal function that builds simulated data, computes
ATT(g,t)'s and some aggregations. It is useful for testing the inference
procedures in the did function.
sim(
sp_list,
ret = NULL,
bstrap = TRUE,
cband = TRUE,
control_group = "nevertreated",
xformla = ~X,
est_method = "dr",
clustervars = NULL,
panel = TRUE
)When ret=NULL, returns the results of the call to att_gt, otherwise returns
1 if the specified test rejects or 0 if not.
A list of simulation parameters. See reset.sim to generate
some default values for parameters
which type of results to return. The options are Wpval (returns
1 if the p-value from a Wald test that all pre-treatment ATT(g,t)'s are equal
is less than .05),
cband (returns 1 if a uniform confidence band covers 0 for groups and times),
simple (returns 1 if, using the simple treatment effect aggregation results
in rejecting that this aggregated treatment effect parameter is equal to 0),
dynamic (returns 1 if the uniform confidence band from the dynamic treatment
effect aggregation covers 0 in all pre- and post-treatment periods). The default
value is NULL, and in this case the function will just return the results from
the call to att_gt.
whether or not to use the bootstrap to conduct inference (default is TRUE)
whether or not to compute uniform confidence bands in the call to att_gt
(the default is TRUE)
Whether to use the "nevertreated" comparison group (the default) or the "notyettreated" as the comparison group
Formula for covariates in att_gt (default is ~X)
Which estimation method to use in att_gt (default is "dr")
Any additional variables which should be clustered on
whether to simulate panel data (the default) or otherwise repeated cross sections data