Cross-Sectional Regression of CARs — cross_sectional

Regress cumulative abnormal returns (CARs) on firm characteristics to explain cross-sectional variation in event effects. Supports OLS with heteroskedasticity-consistent (HC) standard errors.

Usage

cross_sectional_regression(
  task,
  formula,
  data,
  car_window = NULL,
  robust = TRUE
)

Arguments

task: A fitted EventStudyTask with abnormal returns computed.
formula: A formula with the response on the left (ignored; CAR is always the dependent variable) and explanatory variables on the right, e.g., ~ log_market_cap + leverage.
data: A data frame of firm characteristics. Must contain an event_id column to merge with CARs.
car_window: Two-element integer vector specifying the CAR window as c(start, end) relative indices. Default is the full event window.
robust: Logical. If TRUE and the sandwich package is available, compute HC1 robust standard errors. Default TRUE.

Value

A list with class "es_cross_sectional" containing:

model: The fitted lm object
coefficients: Coefficient table with (robust) standard errors
r_squared: R-squared of the regression
n_obs: Number of observations
car_data: The merged CAR + characteristics data