NonlinearDiD (version 0.1.0)

sim_binary_panel: 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
)

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).

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.

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)