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.
Arguments
- object
A model object of class
fixest, where thei()operator has been used to facilitate an "event-study" DiD design. See Examples.- period
Keyword string or numeric sequence. Which group of periods are we aggregating? Accepts the following convenience strings:
"post"(the default),"pre","both", or"diff(for the difference between the post and pre periods). Alternatively, can also be a numeric sequence that designates an explicit subset of periods in the data (e.g.6:8).- rhs
Numeric. The null hypothesis value. Defaults to 0.
- 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. IfFALSE, then the term column will retain the full hypothesis test string as per usual withmarginaleffects::hypotheses(). 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.
See also
marginaleffects::hypotheses(), which this function is ultimately a
convenience wrapper around.
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))
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 3.91 0.86 4.54 <0.001 17.5 2.22 5.59
#>
#> Term: post-treatment (mean)
#>
# The underlying hypothesis is saved as an attribute
attr(post_mean, "hypothesis")
#> post
#> "(`period::6:treat` + `period::7:treat` + `period::8:treat` + `period::9:treat` + `period::10:treat`)/5 = 0"
# Other hypothesis and aggregation options
aggr_es(est, period = "pre") # pre period instead of post
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> -1.18 0.856 -1.38 0.168 2.6 -2.86 0.498
#>
#> Term: pre-treatment (mean)
#>
aggr_es(est, period = "both") # pre & post periods separately
#>
#> Term Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> pre-treatment (mean) -1.18 0.856 -1.38 0.168 2.6 -2.86 0.498
#> post-treatment (mean) 3.91 0.860 4.54 <0.001 17.5 2.22 5.592
#>
#>
aggr_es(est, period = "diff") # post vs pre difference
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 5.09 0.472 10.8 <0.001 87.5 4.16 6.01
#>
#> Term: difference (post vs pre mean)
#>
aggr_es(est, period = 6:8) # specific subset of periods
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 2.73 0.93 2.94 0.00331 8.2 0.909 4.55
#>
#> Term: periods 6:8 (mean)
#>
aggr_es(est, period = "pre", rhs = -1) # pre period with H0 value of 1
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> -0.18 0.856 -0.21 0.834 0.3 -1.86 1.5
#>
#> Term: pre-treatment (mean)
#>
aggr_es(est, aggregation = "cumulative") # cumulative instead of mean effects
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> 19.5 4.3 4.54 <0.001 17.5 11.1 28
#>
#> Term: post-treatment (cumulative)
#>
# Etc.