ipwdid: Inverse probability weighted DiD estimators for the ATT

In DRDID: Doubly Robust Difference-in-Differences Estimators

Inverse probability weighted DiD estimators for the ATT

Description

ipwdid computes the inverse probability weighted estimators for the average treatment effect on the treated in difference-in-differences (DiD) setups. It can be used with panel or stationary repeated cross-sectional data, with or without normalized (stabilized) weights. See Abadie (2005) and Sant'Anna and Zhao (2020) for details.

Usage

ipwdid(
  yname,
  tname,
  idname,
  dname,
  xformla = NULL,
  data,
  panel = TRUE,
  normalized = TRUE,
  weightsname = NULL,
  boot = FALSE,
  boot.type = c("weighted", "multiplier"),
  nboot = 999,
  inffunc = FALSE
)

Arguments

yname	The name of the outcome variable.
tname	The name of the column containing the time periods.
idname	The name of the column containing the unit id name.
dname	The name of the column containing the treatment group (=1 if observation is treated in the post-treatment, =0 otherwise)
xformla	A formula for the covariates to include in the model. It should be of the form ~ X1 + X2 (intercept should not be listed as it is always automatically included). Default is NULL which is equivalent to xformla=~1.
data	The name of the data.frame that contains the data.
panel	Whether or not the data is a panel dataset. The panel dataset should be provided in long format – that is, where each row corresponds to a unit observed at a particular point in time. The default is TRUE. When panel = FALSE, the data is treated as stationary repeated cross sections.
normalized	Logical argument to whether IPW weights should be normalized to sum up to one. Default is TRUE.
weightsname	The name of the column containing the sampling weights. If NULL, then every observation has the same weights.
boot	Logical argument to whether bootstrap should be used for inference. Default is FALSE and analytical standard errors are reported.
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 ipwdid function implements the inverse probability weighted (IPW) difference-in-differences (DiD) estimator for the average treatment effect on the treated (ATT) proposed by Abadie (2005) (normalized = FALSE) or Hajek-type version defined in equations (4.1) and (4.2) in Sant'Anna and Zhao (2020), when either panel data or stationary repeated cross-sectional data are available. This estimator makes use of a logistic propensity score model for the probability of being in the treated group, and the propensity score parameters are estimated via maximum likelihood.

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 in the call (panel, normalized, 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

# -----------------------------------------------
# Panel data case
# -----------------------------------------------
# Form the Lalonde sample with CPS comparison group
eval_lalonde_cps <- subset(nsw_long, nsw_long$treated == 0 | nsw_long$sample == 2)
# Further reduce sample to speed example
set.seed(123)
unit_random <- sample(unique(eval_lalonde_cps$id), 5000)
eval_lalonde_cps <- eval_lalonde_cps[eval_lalonde_cps$id %in% unit_random,]

# Implement IPW DiD with panel data (normalized weights)
ipwdid(yname="re", tname = "year", idname = "id", dname = "experimental",
      xformla= ~ age+ educ+ black+ married+ nodegree+ hisp+ re74,
      data = eval_lalonde_cps, panel = TRUE)

# -----------------------------------------------
# Repeated cross section case
# -----------------------------------------------
# use the simulated data provided in the package
#Implement IPW DiD with repeated cross-section data (normalized weights)
# use Bootstrap to make inference with 199 bootstrap draws (just for illustration)
ipwdid(yname="y", tname = "post", idname = "id", dname = "d",
      xformla= ~ x1 + x2 + x3 + x4,
      data = sim_rc, panel = FALSE,
      boot = TRUE, nboot = 199)

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