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Simulate a panel from a low-rank factor model

Source: R/sim.R
sim_panel.Rd

Generates a long panel whose untreated potential outcomes follow an interactive-fixed-effects (factor) model on top of two-way fixed effects, in the spirit of the data-generating processes in Athey, Imbens, Qu & Viviano (2026). A block treatment is applied to n_treated units from period t0 onward, with a constant additive effect att.

Usage

sim_panel(
  N = 30,
  T = 20,
  n_treated = 5,
  t0 = NULL,
  rank = 3L,
  att = 1,
  noise = 0.5,
  seed = NULL,
  n_cov = 0L,
  phi = NULL,
  confounding = 0
)

Arguments

N

Number of units.

T

Number of periods.

n_treated

Number of treated units.

t0

First treated period (block design).

rank

Number of latent factors.

att

True treatment effect added to treated cells.

noise

Standard deviation of idiosyncratic noise.

seed

Optional RNG seed.

n_cov

Number of time-varying covariates to generate (default 0, none). Generated covariates are cell-level standard-normal draws whose linear index is part of the untreated potential outcome Y(0), matching the additive covariate model of trop() (L = X.phi + R); pass their names to covariates in trop() / panel_compare() to recover the coefficients. Defaults to length(phi) when phi is supplied.

phi

Optional numeric coefficient vector for the covariates (length n_cov). When NULL and n_cov > 0, coefficients are drawn from a standard normal. The coefficients actually used are returned on the "phi" attribute of the result.

confounding

Non-negative strength of selection-on-confounders in the treatment assignment. With confounding = 0 (default) the first n_treated units are treated independently of the latent factors and covariates, so treatment is ignorable and DID/TWFE is unbiased. With confounding > 0 treated units are selected on their latent factor loadings and, when covariates are present (n_cov > 0), on their post-period covariate index \(X'\phi\) as well. The factor part biases estimators without a low-rank term (DID, SDID, SC); the covariate part biases estimators that ignore covariates (SDID, SC, uncontrolled DID). A fully robust estimator (e.g. TROP with covariates=) removes both. Larger values give stronger selection.

Value

A long data.frame with columns id, t, y, w, and the noiseless counterfactual y0 (useful for evaluating estimators). When n_cov > 0 it additionally carries covariate columns x1, ..., x{n_cov}, and the true coefficients are stored on attr(., "phi").

Examples

df <- sim_panel(N = 16, T = 9, n_treated = 3, t0 = 6, seed = 42)
head(df)
#>    id t          y w          y0
#> 1   1 1 1.99808336 0  1.99808336
#> 17  1 2 0.45625369 0  0.45625369
#> 33  1 3 0.08249937 0  0.08249937
#> 49  1 4 0.60979037 0  0.60979037
#> 65  1 5 0.39818588 0  0.39818588
#> 81  1 6 0.75972041 1 -0.24027959

# Two covariates with known coefficients, recoverable by trop(covariates=):
dc <- sim_panel(N = 16, T = 9, n_treated = 3, t0 = 6,
                n_cov = 2, phi = c(1.2, -0.7), seed = 1)
attr(dc, "phi")
#>   x1   x2 
#>  1.2 -0.7