compute.aggte: Compute Aggregated Treatment Effect Parameters
In did: Treatment Effects with Multiple Periods and Groups

compute.aggte

R Documentation

Compute Aggregated Treatment Effect Parameters

Description

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

Usage

compute.aggte(
  MP,
  type = "group",
  balance_e = NULL,
  min_e = -Inf,
  max_e = Inf,
  na.rm = FALSE,
  bstrap = NULL,
  biters = NULL,
  cband = NULL,
  alp = NULL,
  clustervars = NULL,
  call = NULL
)

Arguments

`MP`	an MP object (i.e., the results of the `att_gt()` method)
`type`	Which type of aggregated treatment effect parameter to compute. One option is "simple" (this just computes a weighted average of all group-time average treatment effects with weights proportional to group size). Other options are "dynamic" (this computes average effects across different lengths of exposure to the treatment and is similar to an "event study"; here the overall effect averages the effect of the treatment across all positive lengths of exposure); "group" (this is the default option and computes average treatment effects across different groups; here the overall effect averages the effect across different groups); and "calendar" (this computes average treatment effects across different time periods; here the overall effect averages the effect across each time period).
`balance_e`	If set (and if one computes dynamic effects), it balances the sample with respect to event time. For example, if `balance.e=2`, `aggte` will drop groups that are not exposed to treatment for at least three periods. (the initial period when `e=0` as well as the next two periods when `e=1` and the `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.
`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.
`biters`	The number of bootstrap iterations to use. The default is 1000, and this is only applicable if `bstrap=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-alp`. In order to compute uniform confidence bands, `bstrap` must also be set to `TRUE`. The default is `TRUE`.
`alp`	the significance level, default is 0.05
`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`).
`call`	The function call to aggte