Two-way fixed effects DiD estimator, with repeated cross-section data

Source: R/twfe_did_rc.R

twfe_did_rc is used to compute linear two-way fixed effects estimators for the ATT in difference-in-differences (DiD) setups with stationary repeated cross-sectional 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_rc(
  y,
  post,
  D,
  covariates = NULL,
  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 period, =0 otherwise).

covariates

An \(n\) x \(k\) matrix of covariates to be used in the regression estimation. We will always include an intercept.

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.

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

# use the simulated data provided in the package
covX = as.matrix(sim_rc[,5:8])
# Implement TWFE DiD estimator (you probably should consider something else....)
twfe_did_rc(y = sim_rc$y, post = sim_rc$post, D = sim_rc$d,
           covariates= covX)
#> $ATT
#>   dd:post 
#> -25.43961 
#> 
#> $se
#> [1] 3.710505
#> 
#> $uci
#>   dd:post 
#> -18.16702 
#> 
#> $lci
#>  dd:post 
#> -32.7122 
#> 
#> $boots
#> NULL
#> 
#> $att.inf.func
#> NULL
#>