NonlinearDiD (version 0.1.0)

binary_did_logit: Binary Outcome DiD: Logit Estimator

Description

Estimates a 2x2 difference-in-differences model with a binary outcome using logistic regression on the log-odds scale, reporting both the log-odds DiD coefficient and the average partial effect (APE) on the probability scale.

Usage

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

Value

A list of class binary_did_logit.

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").

Examples

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