sim_binary_panel: Simulate Binary Panel Data with Staggered Treatment

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

Simulate Binary Panel Data with Staggered Treatment

Description

Generates a simulated panel dataset with staggered treatment adoption and a binary outcome. Useful for testing and illustrating nonlinear DiD methods.

The data-generating process is:

Y_{it} = \mathbf{1}\{ \alpha_i + \lambda_t + \delta_{it} \cdot D_{it} + \epsilon_{it} > 0 \}

where \alpha_i is a unit fixed effect, \lambda_t is a time fixed effect, \delta_{it} is the treatment effect (heterogeneous across cohorts), and \epsilon_{it} is logistic noise.

Usage

sim_binary_panel(
  n = 500L,
  nperiods = 6L,
  prop_treated = 0.5,
  n_cohorts = 3L,
  true_att = 0.3,
  base_prob = 0.3,
  unit_fe_sd = 0.5,
  add_covariates = TRUE,
  seed = NULL
)

Arguments

n	Integer. Number of units. Default 500.
nperiods	Integer. Number of time periods. Default 6.
prop_treated	Numeric. Proportion of units ever treated. Default 0.5.
n_cohorts	Integer. Number of treatment cohorts (groups). Default 3.
true_att	Numeric or vector. True ATT for each cohort. Default 0.3.
base_prob	Numeric. Baseline probability P(Y=1) for untreated. Default 0.3.
unit_fe_sd	Numeric. Std. dev. of unit fixed effects. Default 0.5.
add_covariates	Logical. Add pre-treatment covariates. Default TRUE.
seed	Integer. Random seed. Default NULL.

Value

A data frame in long format. Columns: id (unit identifier), period (time period 1 to nperiods), y (binary outcome 0/1), g (treatment cohort; 0 = never treated), D (treatment indicator), x1 and x2 (covariates, if add_covariates = TRUE), and alpha_i (true unit fixed effect, for validation).

Examples

dat <- sim_binary_panel(n = 1000, nperiods = 8, prop_treated = 0.6,
                         n_cohorts = 4, true_att = c(0.2, 0.4, 0.3, 0.5))
head(dat)
table(dat$g)

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