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.

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. 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.) An $$n$$ x $$1$$ vector of Group indicators (=1 if observation is treated in the post-treatment, =0 otherwise). 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. An $$n$$ x $$1$$ vector of weights to be used. If NULL, then every observation has the same weights. Logical argument to whether bootstrap should be used for inference. Default is FALSE. Type of bootstrap to be performed (not relevant if boot = FALSE). Options are "weighted" and "multiplier". If boot = TRUE, default is "weighted". Number of bootstrap repetitions (not relevant if boot = FALSE). Default is 999. Logical argument to whether influence function should be returned. Default is FALSE.

## Value

A list containing the following components:

ATT

The TR-DR DID point estimate

se

The TR-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")

## 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.

## 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$d, covariates= covX) #> Call: #> drdid_rc(y = sim_rc$y, post = sim_rc$post, D = sim_rc$d, covariates = covX)
#> ------------------------------------------------------------------
#>  Locally efficient DR DID estimator for the ATT:
#>
#>    ATT     Std. Error  t value    Pr(>|t|)  [95% Conf. Interval]
#>  -0.1678     0.2009    -0.8352     0.4036    -0.5616     0.226
#> ------------------------------------------------------------------
#>  Estimator based on (stationary) repeated cross-sections data.
#>  Outcome regression est. method: OLS.
#>  Propensity score est. method: maximum likelihood.
#>  Analytical standard error.
#> ------------------------------------------------------------------
#>  See Sant'Anna and Zhao (2020) for details.