Aggregates post- (and/or pre-) treatment effects of an
"event-study" estimation, also known as a dynamic difference-in-differences
(DDiD) model. The event-study should have been estimated using the fixest
package, which provides a specialised i() operator for this class
of models. By default, the function will return the average post-treatment
effect (i.e. across multiple periods). However, it can also return the
cumulative post-treatment effect and can be used to aggregate pre-treatment
effects too. At its heart, aggr_es() is a convenience wrapper around
marginaleffects::hypotheses(), which is used to perform the underlying
joint hypothesis test.
Usage
aggr_es(
object,
rhs = 0,
period = "post",
aggregation = c("mean", "cumulative"),
abbr_term = TRUE,
...
)Arguments
- object
A model object of class
fixest, where thei()operator has been used to facilitate an "event-study" DiD design. See Examples.- rhs
Numeric. The null hypothesis value. Defaults to 0.
- period
Keyword string or numeric sequence. Which group of periods are we aggregating? Can either be one of three convenience strings---i.e., "post" (the default), "prep", or "both"---or a numeric sequence that matches a subset of periods in the data (e.g. 6:8).
- aggregation
Character string. The aggregation type. Either "mean" (the default) or "cumulative".
- abbr_term
Logical. Should the leading "term" column of the return data frame be abbreviated? The default is TRUE. If FALSE, then the term column will retain the full hypothesis test string as per usual with
marginaleffects(). Note that this information is retained as an attribute of the return object, regardless.- ...
Additional arguments passed to
marginaleffects::hypotheses().
Value
A "tidy" data frame of aggregated (pre and/or post) treatment effects, plus inferential information about standard errors, confidence intervals, etc. Potentially useful information about the underlying hypothesis test is also provided as an attribute. See Examples.
Examples
library(ggfixest) ## Will load fixest too
est = feols(y ~ x1 + i(period, treat, 5) | id + period, base_did)
# Default hypothesis test is a null mean post-treatment effect
(post_mean = aggr_es(est))
#> term estimate std.error statistic p.value s.value
#> 1: post-treatment (mean) 3.906554 0.8598576 4.543257 5.539159e-06 17.4619
#> conf.low conf.high
#> 1: 2.221264 5.591844
# The underlying hypothesis is saved as an attribute
attributes(post_mean)["hypothesis"]
#> $<NA>
#> NULL
#>
# Other hypothesis and aggregation options
aggr_es(est, aggregation = "cumulative") # cumulative instead of mean effects
#> term estimate std.error statistic p.value
#> 1: post-treatment (cumulative) 19.53277 4.299288 4.543257 5.539158e-06
#> s.value conf.low conf.high
#> 1: 17.4619 11.10632 27.95922
aggr_es(est, period = "pre") # pre period instead of post
#> term estimate std.error statistic p.value s.value
#> 1: pre-treatment (mean) -1.179871 0.8561964 -1.378037 0.1681917 2.571821
#> conf.low conf.high
#> 1: -2.857985 0.4982434
aggr_es(est, period = "both") # pre & post periods separately
#> term estimate std.error statistic p.value s.value
#> 1: pre-treatment (mean) -1.179871 0.8561964 -1.378037 1.681917e-01 2.571821
#> 2: post-treatment (mean) 3.906554 0.8598576 4.543257 5.539159e-06 17.461902
#> conf.low conf.high
#> 1: -2.857985 0.4982434
#> 2: 2.221264 5.5918440
aggr_es(est, period = 6:8) # specific subset of periods
#> term estimate std.error statistic p.value s.value
#> 1: periods6:8-treatment (mean) 2.731705 0.9299503 2.937474 0.003308975 8.2394
#> conf.low conf.high
#> 1: 0.9090361 4.554374
aggr_es(est, rhs = -1, period = "pre") # pre period with H0 value of 1
#> term estimate std.error statistic p.value s.value
#> 1: pre-treatment (mean) -0.1798707 0.8561964 -0.2100811 0.8336044 0.2625653
#> conf.low conf.high
#> 1: -1.857985 1.498243
# Etc.