run_es: Event Study Estimation for Panel Data

In fixes: Tools for Creating and Visualizing Fixed-Effects Event Study Models

Event Study Estimation for Panel Data

Description

run_es() is a user-friendly event study wrapper for panel data, supporting both classic (universal timing) and staggered (unit-varying timing) settings. It generates lead/lag dummy variables around treatment, builds the regression formula, and returns tidy results with confidence intervals for arbitrary levels.

Usage

run_es(
  data,
  outcome,
  treatment,
  time,
  timing,
  fe,
  lead_range = NULL,
  lag_range = NULL,
  covariates = NULL,
  cluster = NULL,
  weights = NULL,
  baseline = -1,
  interval = 1,
  time_transform = FALSE,
  unit = NULL,
  staggered = FALSE,
  conf.level = 0.95
)

Arguments

data	A data.frame with panel data. Must contain all required variables.
outcome	Outcome variable (unquoted variable name or expression, e.g. log(y)).
treatment	Treatment indicator (unquoted; 0/1 or logical).
time	Time variable (unquoted; numeric or Date).
timing	Treatment timing: a numeric or Date for universal timing, or a variable (unquoted) for staggered.
fe	One-sided formula for fixed effects (e.g. ~ id + year).
lead_range	Number of pre-treatment periods to include (default: detected from data).
lag_range	Number of post-treatment periods to include (default: detected from data).
covariates	One-sided formula of additional controls (optional).
cluster	Cluster variable(s), as a one-sided formula or character vector (optional).
weights	Observation weights (formula, character, or unquoted variable).
baseline	Baseline period for normalization (default: -1, i.e., one period before treatment).
interval	Numeric; interval of the time variable (default: 1).
time_transform	Logical; if TRUE, converts time variable to consecutive integer within units (requires unit).
unit	Panel unit variable (required if time_transform = TRUE).
staggered	Logical; if TRUE, treats timing as variable (default: FALSE).
conf.level	Numeric vector of confidence levels (e.g. c(0.90, 0.95, 0.99); default: 0.95).

Details

Runs an event study regression on panel data, automatically generating lead/lag dummies around a treatment event.

The function does not require explicit formula specification; dummy variables and model formula are constructed internally. Baseline normalization (reference category) can be customized. Untreated units or units never treated are supported. Missing values in key variables result in an error.

Value

A data.frame with class "es_result", containing:

term

Dummy variable name (leadX, lagY)

estimate, std.error, statistic, p.value

Coefficient estimates, standard errors, test statistics, and p-values

conf_low_XX, conf_high_XX

Confidence intervals at each requested level

relative_time

Period relative to treatment (0 = event)

is_baseline

Logical, is this the baseline period (estimate always 0)

Attributes: lead_range, lag_range, baseline, interval, call, model_formula, conf.level.

Key Features

• One-step event study: specify outcome, treatment, time, timing, and fixed effects directly.

• Supports both universal and staggered treatment timing.

• Flexible covariate, clustering, and weighting options.

• Customizable lead/lag window and baseline normalization.

• Returns a tidy data.frame (class "es_result") with estimates and confidence intervals.

Author(s)

Yosuke Abe

See Also

plot_es

Examples

## Not run: 
# Simulated example
result <- run_es(
  data       = panel_data,
  outcome    = y,
  treatment  = treated_1998,
  time       = year,
  timing     = 1998,
  fe         = ~ firm_id + year,
  cluster    = ~ state_id,
  lead_range = 3,
  lag_range  = 3,
  conf.level = c(0.90, 0.95, 0.99)
)
plot_es(result)

## End(Not run)

fixes documentation built on Aug. 9, 2025, 1:09 a.m.