twfe_did_panel
is used to compute linear two-way fixed effects estimators for the ATT
in difference-in-differences (DiD) setups with panel data. As illustrated by Sant'Anna and Zhao (2020),
this estimator generally do not recover the ATT. We encourage empiricists to adopt alternative specifications.
twfe_did_panel(
y1,
y0,
D,
covariates,
i.weights = NULL,
boot = FALSE,
boot.type = "weighted",
nboot = NULL,
inffunc = FALSE
)
An \(n\) x \(1\) vector of outcomes from the post-treatment period.
An \(n\) x \(1\) vector of outcomes from the 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 regression estimation.
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.
A list containing the following components:
The TWFE DiD point estimate
The TWFE DiD standard error
Estimate of the upper bound of a 95% CI for the TWFE parameter.
Estimate of the lower bound of a 95% CI for the TWFE parameter.
All Bootstrap draws of the ATT, in case bootstrap was used to conduct inference. Default is NULL
Estimate of the influence function. Default is NULL
# Form the Lalonde sample with CPS comparison group
eval_lalonde_cps <- subset(nsw, nsw$treated == 0 | nsw$sample == 2)
# Further reduce sample to speed example
set.seed(123)
unit_random <- sample(1:nrow(eval_lalonde_cps), 5000)
eval_lalonde_cps <- eval_lalonde_cps[unit_random,]
# Select some covariates
covX = as.matrix(cbind(eval_lalonde_cps$age, eval_lalonde_cps$educ,
eval_lalonde_cps$black, eval_lalonde_cps$married,
eval_lalonde_cps$nodegree, eval_lalonde_cps$hisp,
eval_lalonde_cps$re74))
# Implement TWFE DiD with panel data
twfe_did_panel(y1 = eval_lalonde_cps$re78, y0 = eval_lalonde_cps$re75,
D = eval_lalonde_cps$experimental,
covariates = covX)
#> $ATT
#> dd:post
#> 702.58
#>
#> $se
#> [1] 595.31
#>
#> $uci
#> dd:post
#> 1869.388
#>
#> $lci
#> dd:post
#> -464.2276
#>
#> $boots
#> NULL
#>
#> $att.inf.func
#> NULL
#>