Two-way fixed effects DiD estimator, with panel data

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
)

Arguments

y1

An \(n\) x \(1\) vector of outcomes from the post-treatment period.

y0

An \(n\) x \(1\) vector of outcomes from the 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 regression estimation.

i.weights

An \(n\) x \(1\) vector of weights to be used. If NULL, then every observation has the same weights.

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 TWFE DiD point estimate

se

The TWFE DiD standard error

uci

Estimate of the upper bound of a 95% CI for the TWFE parameter.

lci

Estimate of the lower bound of a 95% CI for the TWFE parameter.

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

Examples

# 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
#>