count_did_poisson: Count Outcome DiD: Poisson Estimator

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

Count Outcome DiD: Poisson Estimator

Description

Estimates DiD for count outcomes using a Poisson quasi-maximum likelihood (QMLE) estimator with a log-linear parallel trends assumption. The treatment effect is a multiplicative rate ratio.

Usage

count_did_poisson(
  data,
  yname,
  tname,
  idname,
  treat_period,
  control_period,
  dname = NULL,
  gname = NULL,
  xformla = ~1,
  offset = NULL,
  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.
offset	Character. Name of offset variable. Default NULL.
se_type	Character. SE type: "robust" (default), "cluster", or "analytical".
cluster_var	Character. Clustering variable (if se_type = "cluster").

Value

A list of class count_did_poisson.

Examples

dat <- sim_count_panel(n = 400, nperiods = 6, prop_treated = 0.4)
dat2 <- dat[dat$period %in% c(2, 4), ]
res <- count_did_poisson(dat2, "y", "period", "id", 4, 2, gname = "g")
print(res)

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