Generate Random Panel Data for FETWFE Simulations (core)

Generates a random panel data set for simulation studies of the fused extended two-way fixed effects (FETWFE) estimator. 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. When gen_ints = TRUE the full design matrix is returned (including interactions between covariates and fixed effects and treatment indicators). When gen_ints = FALSE the design matrix is generated in a simpler format (with no interactions) as expected by fetwfe(). Moreover, 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

simulateDataCore(
  N,
  T,
  G = NULL,
  d,
  sig_eps_sq,
  sig_eps_c_sq,
  beta,
  seed = NULL,
  gen_ints = FALSE,
  distribution = "gaussian",
  guarantee_rank_condition = FALSE,
  assignment_type = "marginal",
  assignment_coefs = NULL,
  R = NULL
)

Arguments

N

Integer. Number of units in the panel.

T

Integer. Number of time periods.

G

Integer. Number of treated cohorts (with treatment starting in periods 2 to T).

d

Integer. Number of time-invariant covariates.

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).

beta

Numeric vector. Coefficient vector for data generation. Its required length depends on the value of gen_ints:

If gen_ints = TRUE and d > 0, the expected length is \(p = G + (T-1) + d + dG + d(T-1) + num\_treats + num\_treats \times d\), where \(num\_treats = T \times G - \frac{G(G+1)}{2}\).
If gen_ints = TRUE and d = 0, the expected length is \(p = G + (T-1) + num\_treats\).
If gen_ints = FALSE, the expected length is \(p = G + (T-1) + d + num\_treats\).

seed

(Optional) Integer. Seed for reproducibility.

gen_ints

Logical. If TRUE, generate the full design matrix with interactions; if FALSE (the default), generate a design matrix without any interaction terms.

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).

assignment_type

Character. One of "marginal" (default), "multinomial", or "ordered". Selects the cohort-assignment DGP. "marginal" preserves the pre-1.14.0 behavior. The non-marginal types require a non-NULL assignment_coefs argument (typically pulled from a FETWFE_coefs object built with the matching assignment_type).

assignment_coefs

Optional list returned by .gen_assignment_coefs() (an internal helper). Required when assignment_type != "marginal".

R

Deprecated. The former name for G; still accepted with a deprecation warning, and will be removed in a future release. Use G.

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. When gen_ints = TRUE, \(X\) has \(p\) columns with interactions; when gen_ints = FALSE, \(X\) has no 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

When gen_ints = TRUE, the function constructs the design matrix by first generating base fixed effects and a long-format covariate matrix (via generateBaseEffects()), then appending interactions between the covariates and cohort/time fixed effects (via generateFEInts()) and finally treatment indicator columns and treatment-covariate interactions (via genTreatVarsSim() and genTreatInts()). When gen_ints = FALSE, the design matrix consists only of the base fixed effects, covariates, and treatment indicators.

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 \([-\sqrt{3}, \sqrt{3}]\).

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

References