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Generate Random Panel Data for FETWFE Simulations

Source: R/gen_data.R
simulateData.Rd

Generates a random panel data set for simulation studies of the fused extended two-way fixed effects (FETWFE) estimator by taking an object of class "FETWFE_coefs" (produced by genCoefs()) and using it to simulate data. The function creates a balanced panel with \(N\) units over \(T\) time periods, assigns treatment status across \(G\) treated cohorts (with equal marginal probabilities for treatment and non-treatment), and constructs a design matrix along with the corresponding outcome. The covariates are generated according to the specified distribution: by default, covariates are drawn from a normal distribution; if distribution = "uniform", they are drawn uniformly from \([-\sqrt{3}, \sqrt{3}]\). When \(d = 0\) (i.e. no covariates), no covariate-related columns or interactions are generated. See the simulation studies section of Faletto (2025) for details.

Usage

simulateData(
  coefs_obj,
  N,
  sig_eps_sq,
  sig_eps_c_sq,
  distribution = "gaussian",
  guarantee_rank_condition = FALSE,
  seed = NULL
)

Arguments

coefs_obj

An object of class "FETWFE_coefs" containing the coefficient vector and simulation parameters.

N

Integer. Number of units in the panel.

sig_eps_sq

Numeric. Variance of the idiosyncratic (observation-level) noise.

sig_eps_c_sq

Numeric. Variance of the unit-level random effects. Must be non-negative; 0 is allowed (yields a panel with no unit-level random effects).

distribution

Character. Distribution to generate covariates. Defaults to "gaussian". If set to "uniform", covariates are drawn uniformly from \([-\sqrt{3}, \sqrt{3}]\). To obtain a matching ground truth from getTes, pass the same distribution value there.

guarantee_rank_condition

(Optional). Logical. If TRUE, the returned data set is guaranteed to have at least d + 1 units per cohort, which is necessary for the final design matrix to have full column rank. Default is FALSE, in which case only >= 1 unit per cohort is required – this permits small cohorts and therefore high-dimensional (p > NT) panels, which fetwfe() fits in its regularized regime (and on which the high-dimensional debiasedATT() path can be exercised, using the known data-generating coefficient vector; recovering the truth requires an adequate number of units).

seed

(Optional) Controls the random-number generator for the simulated panel. As of fetwfe 1.24.0 the default is NULL, which draws from the ambient random-number generator (respecting any preceding set.seed()) and emits a warning; pass an integer for a reproducible panel, or NA to draw from the ambient generator silently. simulateData() no longer reuses coefs_obj$seed (the seed genCoefs() used to build the coefficients). Default NULL.

Value

An object of class "FETWFE_simulated", which is a list containing:

pdata

A dataframe containing generated data that can be passed to fetwfe().

X

The design matrix \(X\), with \(p\) columns with interactions.

y

A numeric vector of length \(N \times T\) containing the generated responses.

covs

A character vector containing the names of the generated features (if \(d > 0\)), or simply an empty vector (if \(d = 0\))

time_var

The name of the time variable in pdata

unit_var

The name of the unit variable in pdata

treatment

The name of the treatment variable in pdata

response

The name of the response variable in pdata

coefs

The coefficient vector \(\beta\) used for data generation.

first_inds

A vector of indices indicating the first treatment effect for each treated cohort.

N_UNTREATED

The number of never-treated units.

assignments

A vector of counts (of length \(G+1\)) indicating how many units fall into the never-treated group and each of the \(G\) treated cohorts.

indep_counts

Independent cohort assignments (for auxiliary purposes).

p

The number of columns in the design matrix \(X\).

N

Number of units.

T

Number of time periods.

G

Number of treated cohorts.

R

Deprecated alias for G, retained for backward compatibility; populated with the same value. Use G. Will be removed in a future release.

d

Number of covariates.

sig_eps_sq

The idiosyncratic noise variance.

sig_eps_c_sq

The unit-level noise variance.

Details

This function extracts simulation parameters from the FETWFE_coefs object and passes them, along with additional simulation parameters, to the internal function simulateDataCore(). It validates that all necessary components are returned and assigns the S3 class "FETWFE_simulated" to the output.

The random draw is controlled by the seed argument, not by coefs_obj$seed. By default (seed = NULL) simulateData() draws from the ambient random-number generator (so a preceding set.seed() is respected and repeated calls return different panels) and emits a warning noting that this default changed in fetwfe 1.24.0. Pass an integer seed for a reproducible panel (the same integer always yields the same panel), or seed = NA to use the ambient generator without the warning. To vary the panel across Monte Carlo replications, pass a different seed each replication.

Passing an explicit numeric seed calls set.seed(seed) internally and leaves the global RNG advanced after the call returns. This is deliberate — it makes a simulation both reproducible (the same seed always yields the same panel) and varying (subsequent draws consume the advanced stream). Use seed = NA (or the default seed = NULL) to draw from / preserve the ambient stream without calling set.seed().

The argument distribution controls the generation of covariates. For "gaussian", covariates are drawn from rnorm; for "uniform", they are drawn from runif on the interval \([-\sqrt{3}, \sqrt{3}]\) (which ensures that the covariates have unit variance regardless of which distribution is chosen).

When \(d = 0\) (i.e. no covariates), the function omits any covariate-related columns and their interactions.

References

Faletto, G (2025). Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions. arXiv preprint arXiv:2312.05985. https://arxiv.org/abs/2312.05985.

Examples

if (FALSE) { # \dontrun{
  # Generate coefficients
  coefs <- genCoefs(G = 5, T = 30, d = 12, density = 0.1, eff_size = 2, seed = 123)

  # Simulate data using the coefficients
  sim_data <- simulateData(coefs, N = 120, sig_eps_sq = 5, sig_eps_c_sq = 5)
} # }