Treatment effect estimation and pre-trend testing in staggered adoption diff-in-diff designs with an imputation approach of Borusyak, Jaravel, and Spiess (2021)
did_imputation(
data,
yname,
gname,
tname,
idname,
first_stage = NULL,
wname = NULL,
wtr = NULL,
horizon = NULL,
pretrends = NULL,
cluster_var = NULL
)A data.frame containing treatment effect term, estimate, standard
error and confidence interval. This is in tidy format.
A data.frame
String. Variable name for outcome. Use fixest c() syntax
for multiple lhs.
String. Variable name for unit-specific date of treatment
(never-treated should be zero or NA).
String. Variable name for calendar period.
String. Variable name for unique unit id.
Formula for Y(0).
Formula following fixest::feols.
Fixed effects specified after "|".
If not specified, then just unit and time fixed effects will be used.
String. Variable name for estimation weights of observations. This is used in estimating Y(0) and also augments treatment effect weights.
Character vector of treatment weight names (see horizon for standard static and event-study weights)
Integer vector of event_time or TRUE. This only applies if wtr is left
as NULL. if specified, weighted averages/sums of treatment effects will be
reported for each of these horizons separately (i.e. tau0 for the treatment
period, tau1 for one period after treatment, etc.).
If TRUE, all horizons are used.
If wtr and horizon are null, then the static treatment effect is calculated.
Integer vector or TRUE. Which pretrends to estimate.
If TRUE, all pretrends are used.
String. Varaible name for clustering groups. If not
supplied, then idname is used as default.
Load example dataset which has two treatment groups and homogeneous treatment effects
# Load Example Dataset
data("df_hom", package="did2s")
You can run a static TWFE fixed effect model for a simple treatment indicator
did_imputation(data = df_hom, yname = "dep_var", gname = "g",
tname = "year", idname = "unit")
#> # A tibble: 1 x 6
#> lhs term estimate std.error conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 dep_var treat 2.00 0.0182 1.97 2.04
Or you can use relative-treatment indicators to estimate an event study estimate
did_imputation(data = df_hom, yname = "dep_var", gname = "g",
tname = "year", idname = "unit", horizon=TRUE)
#> # A tibble: 21 x 6
#> lhs term estimate std.error conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 dep_var 0 1.97 0.0425 1.89 2.05
#> 2 dep_var 1 2.05 0.0434 1.97 2.14
#> 3 dep_var 2 2.03 0.0432 1.95 2.12
#> 4 dep_var 3 1.97 0.0428 1.88 2.05
#> 5 dep_var 4 1.97 0.0420 1.88 2.05
#> 6 dep_var 5 2.03 0.0423 1.95 2.11
#> 7 dep_var 6 2.04 0.0450 1.95 2.13
#> 8 dep_var 7 2.00 0.0437 1.91 2.08
#> 9 dep_var 8 2.02 0.0440 1.93 2.10
#> 10 dep_var 9 1.96 0.0440 1.87 2.04
#> # ... with 11 more rows
Here's an example using data from Cheng and Hoekstra (2013)
# Castle Data
castle <- haven::read_dta("https://github.com/scunning1975/mixtape/raw/master/castle.dta")did_imputation(data = castle, yname = "c(l_homicide, l_assault)", gname = "effyear",
first_stage = ~ 0 | sid + year,
tname = "year", idname = "sid")
#> # A tibble: 2 x 6
#> lhs term estimate std.error conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 l_homicide treat 0.0798 0.0609 -0.0395 0.199
#> 2 l_assault treat 0.0496 0.0513 -0.0510 0.150
The imputation-based estimator is a method of calculating treatment effects in a difference-in-differences framework. The method estimates a model for Y(0) using untreated/not-yet-treated observations and predicts Y(0) for the treated observations hat(Y_it(0)). The difference between treated and predicted untreated outcomes Y_it(1) - hat(Y_it(0)) serves as an estimate for the treatment effect for unit i in period t. These are then averaged to form average treatment effects for groups of it.