An effective treatment approach to DiD with general treatment patterns
didet.Rd
The `didet()` function takes the effective treatment approach to difference-in-differences estimation with general treatment patterns. The method is developed by Yanagi (2023). For more details, see the package vignette with `vignette("didet")` and `vignette("review", package = "didet")`.
Usage
didet(
yname,
dname,
tname,
idname,
xformla = ~1,
data,
specification = "event",
alp = 0.05,
nboot = 1000
)
Arguments
- yname
The name of the outcome variable
- dname
The name of the treatment variable
- tname
The name of the time period
- idname
The name of the unit index
- xformla
A formula for the unit-specific covariates to be included. It should be of the form `xformla = ~ X1 + X2`. Default is `xformla = ~ 1`.
- data
A data.frame of balanced panel data (long format)
- specification
A character specifying the effective treatment function. Options are "once", "event", "number", and "aggregate". Default is "event".
- alp
The significance level. Default is 0.05.
- nboot
The number of bootstrap repetitions. Default is 1000.
Value
A list that contains the following elements.
- ATEM
A data.frame that collects results for ATEM(t,s,e)
- mover
A data.frame that collects results for the probability of mover
- stayer
A data.frame that collects results for the probability of stayer
- figure
A list that contains the ggplot2 figures for ATEM(t,s,e)
References
Yanagi, T., 2023. An effective treatment approach to difference-in-differences with general treatment patterns. arXiv:2212.13226.
Examples
set.seed(1)
data <- datageneration(N = 1000, S = 4)
did_est <- didet(yname = "Y",
dname = "D",
tname = "period",
idname = "id",
xformla = ~ X,
data = data,
specification = "event",
alp = 0.05,
nboot = 1000)