binary_did_probit: Binary Outcome DiD: Probit Estimator

In NonlinearDiD: Staggered Difference-in-Differences with Nonlinear Outcomes

Binary Outcome DiD: Probit Estimator

Description

Estimates 2x2 DiD with binary outcome using probit regression. Parallel trends assumed on the probit (inverse-normal) scale.

Usage

binary_did_probit(
  data,
  yname,
  tname,
  idname,
  treat_period,
  control_period,
  dname = NULL,
  gname = NULL,
  xformla = ~1,
  se_type = c("robust", "cluster", "analytical"),
  cluster_var = NULL
)

Arguments

data	A data frame (long format).
yname	Character. Binary outcome variable name.
tname	Character. Time period variable name.
idname	Character. Unit ID variable name.
treat_period	Numeric. The treatment (post) period.
control_period	Numeric. The pre-treatment baseline period.
dname	Character. Treatment indicator variable name (optional).
gname	Character. Cohort variable name (optional).
xformla	One-sided formula for covariates. Default ~1.
se_type	Character. SE type: "robust" (default), "cluster", or "analytical".
cluster_var	Character. Clustering variable (if se_type = "cluster").

Value

A list of class binary_did_probit.

Examples

dat <- sim_binary_panel(n = 500, nperiods = 4, prop_treated = 0.5)
dat2 <- dat[dat$period %in% c(2, 3), ]
res <- binary_did_probit(dat2, "y", "period", "id", 3, 2, gname = "g")
print(res)

NonlinearDiD documentation built on May 6, 2026, 1:06 a.m.