std_ipw_did_rc: Standardized inverse probability weighted DiD estimator, with...

In DRDID: Doubly Robust Difference-in-Differences Estimators

Standardized inverse probability weighted DiD estimator, with repeated cross-section data

Description

std_ipw_did_rc is used to compute inverse probability weighted (IPW) estimators for the ATT in DID setups with stationary repeated cross-sectional data. IPW weights are normalized to sum up to one, that is, the estimator is of the Hajek type.

Usage

std_ipw_did_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 estimation. Please add a column of ones if you want to include an intercept. 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. The weights are normalized and therefore enforced to have mean 1 across all observations.
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.

Value

A list containing the following components:

ATT	The IPW DID point estimate.
se	The IPW 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 = FALSE, normalized = TRUE, boot, boot.type, nboot, type="ipw")

References

Abadie, Alberto (2005), "Semiparametric Difference-in-Differences Estimators", Review of Economic Studies, vol. 72(1), p. 1-19, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/0034-6527.00321")}.

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(cbind(1, sim_rc[,5:8]))
# Implement normalized IPW DID estimator
std_ipw_did_rc(y = sim_rc$y, post = sim_rc$post, D = sim_rc$d,
               covariates= covX)

DRDID documentation built on Oct. 8, 2024, 1:08 a.m.