DIDparams {did}  R Documentation 
Object to hold did parameters that are passed across functions
DIDparams(
yname,
tname,
idname = NULL,
gname,
xformla = NULL,
data,
control_group,
anticipation = 0,
weightsname = NULL,
alp = 0.05,
bstrap = TRUE,
biters = 1000,
clustervars = NULL,
cband = TRUE,
print_details = TRUE,
pl = FALSE,
cores = 1,
est_method = "dr",
base_period = "varying",
panel = TRUE,
true_repeated_cross_sections,
n = NULL,
nG = NULL,
nT = NULL,
tlist = NULL,
glist = NULL,
call = NULL
)
yname 
The name of the outcome variable 
tname 
The name of the column containing the time periods 
idname 
The individual (crosssectional unit) id name 
gname 
The name of the variable in 
xformla 
A formula for the covariates to include in the
model. It should be of the form 
data 
The name of the data.frame that contains the data 
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

anticipation 
The number of time periods before participating in the treatment where units can anticipate participating in the treatment and therefore it can affect their untreated potential outcomes 
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 
biters 
The number of bootstrap iterations to use. The default is 1000,
and this is only applicable if 
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 
cband 
Boolean for whether or not to compute a uniform confidence
band that covers all of the grouptime average treatment effects
with fixed probability 
print_details 
Whether or not to show details/progress of computations.
Default is 
pl 
Whether or not to use parallel processing 
cores 
The number of cores to use for parallel processing 
est_method 
the method to compute grouptime average treatment effects. The default is "dr" which uses the doubly robust
approach in the 
base_period 
Whether to use a "varying" base period or a "universal" base period. Either choice results in the same posttreatment estimates of ATT(g,t)'s. In pretreatment periods, using a varying base period amounts to computing a pseudoATT in each treatment period by comparing the change in outcomes for a particular group relative to its comparison group in the pretreatment periods (i.e., in pretreatment periods this setting computes changes from period t1 to period t, but repeatedly changes the value of t) A universal base period fixes the base period to always be (ganticipation1). This does not compute pseudoATT(g,t)'s in pretreatment periods, but rather reports average changes in outcomes from period t to (ganticipation1) for a particular group relative to its comparison group. This is analogous to what is often reported in event study regressions. Using a varying base period results in an estimate of ATT(g,t) being reported in the period immediately before treatment. Using a universal base period normalizes the estimate in the period right before treatment (or earlier when the user allows for anticipation) to be equal to 0, but one extra estimate in an earlier period. 
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 
true_repeated_cross_sections 
Whether or not the data really is repeated cross sections. (We include this because unbalanced panel code runs through the repeated cross sections code) 
n 
The number of observations. This is equal to the number of units (which may be different from the number of rows in a panel dataset). 
nG 
The number of groups 
nT 
The number of time periods 
tlist 
a vector containing each time period 
glist 
a vector containing each group 
call 
Function call to att_gt 