staggered_ife: staggered_ife

In bcallaway11/ife: Treatment Effects in Interactive Fixed Effects Models

staggered_ife

Description

Compute treatment effects in interactive fixed effects models with a small number of time periods by exploiting staggered treatment adoption

Usage

staggered_ife(
  yname,
  gname,
  tname,
  idname,
  data,
  nife,
  xformla = ~1,
  ret_ife_regs = TRUE,
  anticipation = 0,
  cband = TRUE,
  alp = 0.05,
  boot_type = "multiplier",
  biters = 100,
  cl = 1
)

Arguments

yname	Name of outcome in data
gname	Name of group in data
tname	Name of time period in data
idname	Name of id in data
data	balanced panel data
nife	the number of interactive fixed effects to include in the model
xformla	Formula for which covariates to include in the model. Default is ~1.
ret_ife_regs	Whether or not to return the first stage ife regressions; default is FALSE.
anticipation	Number of periods that treatment is anticipated. Default is 0. This is in “periods”; e.g., code will work in time periods are equally spaced but 2 years apart. In this case, to allow for treatment anticipation of 2 year (<=> 1 period), set anticipation = 1.
cband	whether or not to compute a uniform (instead of pointwise) confidence band
alp	significance level; default is 0.05
boot_type	should be one of "multiplier" (the default) or "empirical". The multiplier bootstrap is generally much faster, but attgt_fun needs to provide an expression for the influence function (which could be challenging to figure out). If no influence function is provided, then the pte package will use the empirical bootstrap no matter what the value of this parameter.
biters	number of bootstrap iterations; default is 100
cl	number of clusters to be used when bootstrapping; default is 1

Value

ptetools::pte_results object

bcallaway11/ife documentation built on March 1, 2025, 6:56 a.m.