compute_aggregation: Compute Aggregated Treatment Effect Parameters

Description

Does the heavy lifting on computing aggregated group-time average treatment effects

Usage

compute_aggregation(
  ddd_obj,
  type = "simple",
  cluster = NULL,
  balance_e = NULL,
  min_e = -Inf,
  max_e = Inf,
  na.rm = FALSE,
  boot = FALSE,
  nboot = NULL,
  cband = NULL,
  alpha = 0.05
)

Value

Aggregation object (list) of class agg_ddd

Arguments

ddd_obj: a ddd object (i.e., the results of the ddd() function)
type: Which type of aggregated treatment effect parameter to compute. "simple" just computes a weighted average of all group-time average treatment effects with weights proportional to group size. "eventstudy" computes average effects across different lengths of exposure to the treatment (event times). Here the overall effect averages the effect of the treatment across the positive lengths of exposure. This is the default option; "group" computes average treatment effects across different groups/cohorts; here the overall effect averages the effect across different groups using group size as weights; "calendar" computes average treatment effects across different time periods, with weights proportional to the group size; here the overall effect averages the effect across each time period.
cluster: The name of the variable to be used for clustering. The maximum number of cluster variables is 1. Default is NULL.
balance_e: If set (and if one computes event study), it balances the sample with respect to event time. For example, if balance_e=2, agg_ddd will drop groups that are not exposed to treatment for at least three periods, the initial period e=0 as well as the next two periods, e=1 and e=2. This ensures that the composition of groups does not change when event time changes.
min_e: For event studies, this is the smallest event time to compute dynamic effects for. By default, min_e = -Inf so that effects at all lengths of exposure are computed.
max_e: For event studies, this is the largest event time to compute dynamic effects for. By default, max_e = Inf so that effects at all lengths of exposure are computed.
na.rm: Logical value if we are to remove missing Values from analyses. Defaults is FALSE.
boot: Boolean for whether or not to compute standard errors using the multiplier bootstrap. If standard errors are clustered, then one must set boot=TRUE. Default is value set in the ddd object. If boot = FALSE, then analytical standard errors are reported.
nboot: The number of bootstrap iterations to use. The default is the value set in the ddd object, and this is only applicable if boot=TRUE.
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 - alpha. In order to compute uniform confidence bands, boot must also be set to TRUE. The default is the value set in the ddd object
alpha: The level of confidence for the confidence intervals. The default is 0.05. Otherwise, it will use the value set in the ddd object.