Basic two-way fixed-effects DiD estimation

Estimates a classic difference-in-differences model of the form outcome ~ D_it | fe on the package's internal fixed-effects OLS engine (numerically equivalent to fixest::feols()).

did() supersedes run_did() as of fixes 1.0.0.

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)
did(df, outcome = y, treatment = D, fe = ~ id + year)

Option B — timing-based construction (convenience; consistent with event_study() and att()):

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 att(); for dynamic event-study estimates use event_study().

Usage

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.

coeftable

Data frame with all kept coefficients (treatment and covariates): term, estimate, std.error, statistic, p.value.

vcov

Full coefficient covariance matrix.

fit_stats

Model-level statistics (nobs, R-squareds, sigma, logLik, AIC, BIC) used by broom::glance().

df.t

Degrees of freedom of the t inference.

Attributes: call, formula_str, outcome, treatment, timing, fe, vcov_type, cluster_vars, conf.level, N, N_units, N_treated, unit, time. N, N_units, and N_treated describe the estimation sample (after rows with missing values are dropped), matching broom::glance(), not nrow(data).

See also

event_study() for dynamic effects, att() for staggered aggregation, and run_did() for the deprecated verb-style predecessor.

Examples

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

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

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

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