Run a basic two-way fixed-effects DiD model

Estimates a classic difference-in-differences model of the form outcome ~ D_it | fe using fixest::feols().

There are two ways to supply the treatment indicator:

Option A — pre-built D_it (maximum flexibility):

df$D <- as.integer(df$treated & df$year >= 2006)
run_did(df, outcome = y, treatment = D, fe = ~ id + year)

Option B — timing-based construction (convenience; consistent with run_es() and calc_att()):

run_did(df, outcome = y, treatment = treated, time = year, timing = 2006,
        fe = ~ id + year)

Here treatment is a binary group indicator (1 = treated unit, 0 = control), time is the calendar-time variable, and timing is the scalar treatment onset period. Internally D_it = treatment * (time >= timing) is constructed automatically. For staggered-adoption settings use calc_att(); for dynamic event-study estimates use run_es().

Usage

run_did(
  data,
  outcome,
  treatment,
  timing = NULL,
  fe = NULL,
  unit = NULL,
  time = NULL,
  covariates = NULL,
  cluster = NULL,
  weights = NULL,
  conf.level = 0.95,
  vcov = "HC1",
  vcov_args = list()
)

Arguments

data: A data.frame (panel format).
outcome: Unquoted outcome variable or expression (e.g., log(y)).
treatment: Unquoted column name. When timing = NULL (default): a pre-built binary D_it indicator (1 = treated unit-time, 0 = otherwise). When timing is provided: a binary group indicator (1 = treated unit, 0 = control unit; constant within units).
timing: Numeric scalar. When provided, D_it is constructed as treatment * (time >= timing). Requires time to be specified. Default NULL (user supplies pre-built D_it via treatment).
fe: One-sided formula specifying fixed effects, e.g. ~ id + year. If NULL and both unit and time are supplied, fe is auto-inferred as ~ unit + time. If NULL and neither is supplied, a pooled OLS model is estimated (with a message).
unit: Unquoted unit identifier column (for metadata and fe auto-inference).
time: Unquoted time variable column. Used for (a) fe auto-inference and (b) D_it construction when timing is provided.
covariates: One-sided formula of additional controls, e.g. ~ x1 + x2.
cluster: Clustering specification: a one-sided formula (~ id), a single character column name, or a numeric vector of length nrow(data). When cluster is specified and vcov is the default "HC1", cluster-robust standard errors are used automatically.
weights: Observation weights (formula or numeric vector).
conf.level: Confidence level(s) for CIs. Scalar or vector (e.g., c(0.90, 0.95)). Default 0.95.
vcov: VCOV type string passed to fixest::vcov(). Default "HC1". Ignored in favour of cluster-robust SE when cluster is supplied and vcov is left at its default "HC1".
vcov_args: Named list of additional arguments forwarded to fixest::vcov().

Value

A did_result object (named list) with elements:

estimates: Data frame with the treatment coefficient: term, estimate, std.error, statistic, p.value, and conf_low_XX/conf_high_XX for each conf.level entry.
model: The underlying fixest model object.

Attributes: call, formula_str, outcome, treatment, timing, fe, vcov_type, cluster_vars, conf.level, N, N_units, N_treated, unit, time.

Examples

if (FALSE) { # \dontrun{
# Option A: pre-built D_it
df$D <- as.integer(df$treated & df$year >= 2006)
res <- run_did(df, outcome = y, treatment = D, fe = ~ id + year)

# Option B: timing-based construction
res <- run_did(df, outcome = y, treatment = treated, time = year,
               timing = 2006, fe = ~ id + year)

# Cluster-robust SE
res <- run_did(df, outcome = y, treatment = D, fe = ~ id + year,
               cluster = ~ id)

print(res)
broom::tidy(res)
broom::glance(res)
# modelsummary::modelsummary(res)
} # }