Aggregate Group-Time Average Treatment Effects in Staggered Triple-Differences Designs.
agg_ddd.Rdagg_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
)Arguments
- ddd_obj
a
dddobject (i.e., the results of theddd()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_dddwill drop groups that are not exposed to treatment for at least three periods, the initial periode=0as well as the next two periods,e=1ande=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 = -Infso 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 = Infso 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. Ifboot = 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,bootmust also be set toTRUE. 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)
#> Call:
#> agg_ddd(ddd_obj = out, type = "simple", alpha = 0.1)
#> ========================= DDD Aggregation ============================
#> Overall ATT:
#> ATT Std. Error [ 90% Conf. Int.]
#> 19.005 0.3718 18.3935 19.6166 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.1
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.
# Event study aggregation
agg_ddd(out, type = "eventstudy", alpha = 0.10)
#> Call:
#> agg_ddd(ddd_obj = out, type = "eventstudy", alpha = 0.1)
#> ========================= DDD Aggregation ============================
#> Overall summary of ATT's based on event-study/dynamic aggregation:
#> ATT Std. Error [ 90% Conf. Int.]
#> 19.2985 0.3653 18.6976 19.8993 *
#>
#> Event Study:
#> Event time Estimate Std. Error [90% Ptwise. Conf. Band]
#> -1 -0.2292 0.4289 -0.9346 0.4762
#> 0 18.5301 0.4589 17.7752 19.2850 *
#> 1 20.0668 0.4283 19.3623 20.7714 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.1
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.
# Group aggregation
agg_ddd(out, type = "group", alpha = 0.10)
#> Call:
#> agg_ddd(ddd_obj = out, type = "group", alpha = 0.1)
#> ========================= DDD Aggregation ============================
#> Overall summary of ATT's based on group/cohort aggregation:
#> ATT Std. Error [ 90% Conf. Int.]
#> 20.7971 0.2922 20.3166 21.2777 *
#>
#> Group Effects:
#> Group Estimate Std. Error [90% Ptwise. Conf. Band]
#> 2 14.9986 0.3623 14.4026 15.5946 *
#> 3 25.4900 0.4594 24.7344 26.2456 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.1
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.
# Calendar aggregation
agg_ddd(out, type = "calendar", alpha = 0.10)
#> Call:
#> agg_ddd(ddd_obj = out, type = "calendar", alpha = 0.1)
#> ========================= DDD Aggregation ============================
#> Overall summary of ATT's based on calendar time aggregation:
#> ATT Std. Error [ 90% Conf. Int.]
#> 16.4972 0.2783 16.0395 16.955 *
#>
#> Calendar Effects:
#> Time Estimate Std. Error [90% Ptwise. Conf. Band]
#> 2 9.9303 0.4422 9.2029 10.6577 *
#> 3 23.0642 0.3908 22.4214 23.7069 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.1
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.