triplediff (version 0.2.0)

agg_ddd: Aggregate Group-Time Average Treatment Effects in Staggered Triple-Differences Designs.

Description

agg_ddd is a function that take group-time average treatment effects and aggregate them into a smaller number of summary parameters in staggered triple differences designs. There are several possible aggregations including "simple", "eventstudy", "group", and "calendar". Default is "eventstudy".

Usage

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

Value

A object (list) of class agg_ddd that holds the results from the aggregation step.

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.

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 0.95. 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.

Examples

#----------------------------------------------------------
# Triple Diff with multiple time periods
#----------------------------------------------------------

data <- gen_dgp_mult_periods(size = 500, dgp_type = 1)[["data"]]

out <- ddd(yname = "y", tname = "time", idname = "id",
            gname = "state", pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
            data = data, control_group = "nevertreated", base_period = "varying",
            est_method = "dr")
# Simple aggregation
agg_ddd(out, type = "simple", alpha = 0.10)

# Event study aggregation
agg_ddd(out, type = "eventstudy", alpha = 0.10)

# Group aggregation
agg_ddd(out, type = "group", alpha = 0.10)

# Calendar aggregation
agg_ddd(out, type = "calendar", alpha = 0.10)