ordid {DRDID}

Outcome regression DiD estimators for the ATT

Description

ordid computes the outcome regressions estimators for the average treatment effect on the treated in difference-in-differences (DiD) setups. It can be used with panel or repeated cross section data. See Sant'Anna and Zhao (2020) for details.

Usage

ordid(
  yname,
  tname,
  idname,
  dname,
  xformla = NULL,
  data,
  panel = 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.

weightsname

The name of the column containing the sampling weights. 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 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 ordid function implements outcome regression difference-in-differences (DiD) estimator for the average treatment effect on the treated (ATT) defined in equation (2.2) of Sant'Anna and Zhao (2020). The estimator follows the same spirit of the nonparametric estimators proposed by Heckman, Ichimura and Todd (1997), though here the the outcome regression models are assumed to be linear in covariates (parametric).

The nuisance parameters (outcome regression coefficients) are estimated via ordinary least squares.

Value

A list containing the following components:

ATT

The OR DiD point estimate

se

The OR 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=="or")

References

Heckman, James J., Ichimura, Hidehiko, and Todd, Petra E. (1997),"Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme", Review of Economic Studies, vol. 64(4), p. 605–654, doi:10.2307/2971733.

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

# -----------------------------------------------
# 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 OR DiD with panel data
ordid(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 OR DiD with repeated cross-section data
# use Bootstrap to make inference with 199 bootstrap draws (just for illustration)
ordid(yname="y", tname = "post", idname = "id", dname = "d",
      xformla= ~ x1 + x2 + x3 + x4,
      data = sim_rc, panel = FALSE,
      boot = TRUE, nboot = 199)