SimDiD: DiD data simulator with staggered treatment.

Description

Simulate data from the model Y_it = alpha_i + mu_t + ATT*(t >= G_i) + epsilon_it, where i is individual, t is year, and G_i is the cohort. The ATT formula is ATTat0 + EventTime*ATTgrowth + \*cohort_counter\*ATTcohortdiff, where cohort_counter is the order of treated cohort (first, second, etc.).

Usage

SimDiD(
  seed = 1,
  sample_size = 100,
  cohorts = c(2007, 2010, 2012),
  ATTat0 = 1,
  ATTgrowth = 1,
  ATTcohortdiff = 0.5,
  anticipation = 0,
  minyear = 2003,
  maxyear = 2013,
  idvar = 1,
  yearvar = 1,
  shockvar = 1,
  indivAR1 = FALSE,
  time_covars = FALSE,
  clusters = FALSE,
  markets = FALSE,
  randomNA = FALSE,
  missingCohorts = NULL
)

Value

A list with two data.tables. The first data.table is simulated data with variables (id, year, cohort, Y), where Y is the outcome variable. The second data.table contains the true ATT values, both at the (event,cohort) level and by event averaging across cohorts.

Arguments

seed: Set the random seed. Default is seed=1.
sample_size: Number of individuals. Default is sample_size=100.
cohorts: Vector of years at which treatment onset occurs. Default is cohorts=c(2007,2010,2012).
ATTat0: Treatment effect at event time 0. Default is 1.
ATTgrowth: Increment in the ATT for each event time after 0. Default is 1.
ATTcohortdiff: Incrememnt in the ATT for each cohort. Default is 0.5.
anticipation: Number of years prior to cohort to allow 50% treatment effects. Default is anticipation=0.
minyear: Minimum calendar year to include in the data. Default is minyear=2003.
maxyear: Maximum calendar year to include in the data. Default is maxyear=2013.
idvar: Variance of individual fixed effects (alpha_i). Default is idvar=1.
yearvar: Variance of year effects (mu_i). Default is yearvar=1.
shockvar: Variance of idiosyncratic shocks (epsilon_it). Default is shockvar=1.
indivAR1: Each individual's shocks follow an AR(1) process. Default is FALSE.
time_covars: Add 2 time-varying covariates, called "X1" and "X2". Default is FALSE.
clusters: Add 10 randomly assigned clusters, with cluster-specific AR(1) shocks. Default is FALSE.
markets: Add 10 randomly assigned markets, with market-specific shocks that are systematically greater for markets that are treated earlier. Default is FALSE.
randomNA: If TRUE, randomly assign the outcome variable with missing values (NA) in some cases. Default is FALSE.
missingCohorts: If set to a particular cohort (or vector of cohorts), all of the outcomes for that cohort at event time -1 will be set to missing. Default is NULL.

Examples

Run this code

# simulate data with default options
SimDiD()

Run the code above in your browser using DataLab