cross_sectional_regression: Cross-Sectional Regression of CARs

In EventStudy: Event Study Analysis

Cross-Sectional Regression of CARs

Description

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

See Also

Other eventstudy-statistics: ARTTest, BHARTTest, BMPTest, CARTTest, CSectTTest, CalendarTimePortfolioTest, GeneralizedSignTest, KolariPynnonenTest, MultiEventStatisticsSet, PatellZTest, RankTest, SignTest, SingleEventStatisticsSet, StatisticsSetBase, TestStatisticBase, adjust_p_values(), bootstrap_test(), car_by_group(), car_quantiles(), model_diagnostics(), pretrend_test(), simulate_event_study(), validate_task()

EventStudy documentation built on Oct. 31, 2026, 1:07 a.m.