Skip to contents

Simulate Event Study for Power Analysis

Source: R/simulation.R
simulate_event_study.Rd

Runs Monte Carlo simulations to estimate the statistical power (rejection rate) of an event study design. Each simulation generates synthetic data from a user-specified DGP, runs the full event study pipeline, and records whether the null hypothesis is rejected.

Usage

simulate_event_study(
  n_events = 20,
  event_window = c(-5, 5),
  estimation_window_length = 120,
  abnormal_return = 0,
  return_model = MarketModel$new(),
  test_statistic = "CSectT",
  alpha = 0.05,
  n_simulations = 1000,
  dgp_params = list(),
  seed = NULL
)

Arguments

n_events

Number of events (firms) per simulation. Default 20.

event_window

Event window as c(start, end). Default c(-5, 5).

estimation_window_length

Length of the estimation window. Default 120.

abnormal_return

Injected abnormal return on the event day (day 0). Set to 0 for size analysis, positive for power analysis. Default 0.

return_model

An initialized return model object. Default MarketModel$new().

test_statistic

Name of the multi-event test statistic to evaluate. Default "CSectT".

alpha

Significance level. Default 0.05.

n_simulations

Number of Monte Carlo replications. Default 1000.

dgp_params

List of DGP parameters: alpha (drift), beta (market exposure), sigma_firm (idiosyncratic volatility), sigma_market (market volatility).

seed

Optional seed for reproducibility.

Value

An S3 object of class es_simulation with components:

power

Rejection rate at the event day

rejection_by_day

Tibble of rejection rates for each event-window day

test_stats

Vector of test statistics at the event day from each sim

params

List of simulation parameters

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(), cross_sectional_regression(), model_diagnostics(), pretrend_test(), validate_task()