drdid_rc: Locally efficient doubly robust DiD estimator for the ATT,...

In DRDID: Doubly Robust Difference-in-Differences Estimators

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 n x 1 vector of outcomes from the both pre and post-treatment periods.
post	An n x 1 vector of Post-Treatment dummies (post = 1 if observation belongs to post-treatment period, and post = 0 if observation belongs to pre-treatment period.)
D	An n x 1 vector of Group indicators (=1 if observation is treated in the post-treatment, =0 otherwise).
covariates	An n x k matrix of covariates to be used in the propensity score and regression estimation. If covariates = NULL, this leads to an unconditional DiD estimator.
i.weights	An n x 1 vector of weights to be used. If NULL, then every observation has the same weights.
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 boot = FALSE). Options are "weighted" and "multiplier". If boot = TRUE, default is "weighted".
nboot	Number of bootstrap repetitions (not relevant if boot = FALSE). Default is 999.
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, \Sexpr[results=rd]{tools:::Rd_expr_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)

DRDID documentation built on May 31, 2023, 9:10 p.m.