NonlinearDiD (version 0.1.0)

count_did_poisson: 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
)

Value

A list of class count_did_poisson.

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").

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)