drdid_rc {DRDID} | R Documentation |
Locally efficient doubly robust DiD estimator for the ATT, with repeated cross-section data
Description
drdid_rc
is used to compute the locally efficient doubly robust estimators for the ATT
in difference-in-differences (DiD) setups with stationary repeated cross-sectional data.
Usage
drdid_rc(
y,
post,
D,
covariates,
i.weights = NULL,
boot = FALSE,
boot.type = "weighted",
nboot = NULL,
inffunc = FALSE
)
Arguments
y |
An |
post |
An |
D |
An |
covariates |
An |
i.weights |
An |
boot |
Logical argument to whether bootstrap should be used for inference. Default is FALSE. |
boot.type |
Type of bootstrap to be performed (not relevant if |
nboot |
Number of bootstrap repetitions (not relevant if |
inffunc |
Logical argument to whether influence function should be returned. Default is FALSE. |
Details
The drdid_rc
function implements the locally efficient doubly robust difference-in-differences (DiD)
estimator for the average treatment effect on the treated (ATT) defined in equation (3.4)
in Sant'Anna and Zhao (2020). This estimator makes use of a logistic propensity score model for the probability
of being in the treated group, and of (separate) linear regression models for the outcome of both treated and comparison units,
in both pre and post-treatment periods.
The propensity score parameters are estimated using maximum likelihood, and the outcome regression coefficients are estimated using ordinary least squares; see Sant'Anna and Zhao (2020) for details.
Value
A list containing the following components:
ATT |
The DR DiD point estimate |
se |
The DR DiD standard error |
uci |
Estimate of the upper bound of a 95% CI for the ATT |
lci |
Estimate of the lower bound of a 95% CI for the ATT |
boots |
All Bootstrap draws of the ATT, in case bootstrap was used to conduct inference. Default is NULL |
att.inf.func |
Estimate of the influence function. Default is NULL |
call.param |
The matched call. |
argu |
Some arguments used (explicitly or not) in the call (panel = TRUE, estMethod = "trad", boot, boot.type, nboot, type="dr") |
References
Sant'Anna, Pedro H. C. and Zhao, Jun. (2020), "Doubly Robust Difference-in-Differences Estimators." Journal of Econometrics, Vol. 219 (1), pp. 101-122, doi:10.1016/j.jeconom.2020.06.003
Examples
# use the simulated data provided in the package
covX = as.matrix(sim_rc[,5:8])
# Implement the 'traditional' locally efficient DR DiD estimator
drdid_rc(y = sim_rc$y, post = sim_rc$post, D = sim_rc$d,
covariates= covX)