std_ipw_did_rc function

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

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.

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.

Returns

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")

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)

References

Abadie, Alberto (2005), "Semiparametric Difference-in-DifferencesEstimators", 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 RobustDifference-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")}