sim_did: Simulate a Difference-in-Differences (DiD) dataset

Description

Simulates a dataset for a Difference-in-Differences analysis with various customizable options.

Usage

sim_did(
  sample_size,
  time_period,
  untreated_prop = 0.3,
  epsilon_size = 0.001,
  cov = "no",
  hetero = "all",
  second_outcome = FALSE,
  second_cov = FALSE,
  vary_cov = FALSE,
  na = "none",
  balanced = TRUE,
  seed = NA,
  stratify = FALSE,
  treatment_assign = "latent",
  second_cohort = FALSE,
  confound_ratio = 1,
  second_het = "all"
)

Value

A list containing the simulated dataset (dt) and the treatment effect values (att).

Arguments

sample_size: The number of units in the dataset.
time_period: The number of time periods in the dataset.
untreated_prop: The proportion of untreated units.
epsilon_size: The standard deviation for the error term in potential outcomes.
cov: The type of covariate to include ("no", "int", or "cont").
hetero: The type of heterogeneity in treatment effects ("all" or "dynamic").
second_outcome: Whether to include a second outcome variable.
second_cov: Whether to include a second covariate.
vary_cov: include time-varying covariates
na: Whether to generate missing data ("none", "y", "x", or "both").
balanced: Whether to balance the dataset by random sampling.
seed: Seed for random number generation.
stratify: Whether to stratify the dataset based on a binary covariate.
treatment_assign: The method for treatment assignment ("latent" or "uniform").
second_cohort: include confounding events
confound_ratio: extent of event confoundedness
second_het: heterogeneity of the second event

Examples

Run this code

# Simulate a DiD dataset with default settings
data <- sim_did(sample_size = 100, time_period = 5)

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