EventStudy-package: EventStudy: Event Study Analysis in R
In EventStudy: Event Study Analysis

EventStudy-package

R Documentation

EventStudy: Event Study Analysis in R

Description

Perform financial event study analysis in R. Implements the classical event study methodology (MacKinlay 1997) with multiple return models, parametric and non-parametric test statistics, and visualization tools.

Return Models

MarketModel – OLS market model (single factor, optional HAC SEs)
MarketAdjustedModel – Market-adjusted returns
ComparisonPeriodMeanAdjustedModel – Mean-adjusted returns
FamaFrench3FactorModel – Fama-French 3-factor model
FamaFrench5FactorModel – Fama-French 5-factor model
Carhart4FactorModel – Carhart 4-factor model
GARCHModel – GARCH(1,1) model
RollingWindowModel – Rolling-window OLS (time-varying beta)
DCCGARCHModel – DCC-GARCH (time-varying beta)
BHARModel – Buy-and-hold abnormal returns
VolumeModel – Volume event study model
VolatilityModel – Volatility event study model

Test Statistics

Single-event:

ARTTest – Abnormal return t-test
CARTTest – Cumulative abnormal return t-test
BHARTTest – BHAR t-test

Multi-event:

CSectTTest – Cross-sectional t-test
PatellZTest – Patell standardized residual test
SignTest – Sign test
GeneralizedSignTest – Generalized sign test (Cowan 1992)
RankTest – Rank test (Corrado 1989)
BMPTest – Boehmer, Musumeci & Poulsen (1991) test
KolariPynnonenTest – Kolari-Pynnoenen adjusted BMP test
CalendarTimePortfolioTest – Calendar-time portfolio test

Inference & Robustness

adjust_p_values – Multiple testing corrections (BH, Bonferroni, etc.)
bootstrap_test – Wild bootstrap inference
HAC standard errors via use_hac = TRUE in model constructors

Simulation

simulate_event_study – Monte Carlo power analysis

Pipeline

The standard workflow is:

Create an EventStudyTask with data
Define a ParameterSet with model and test choices
Run run_event_study(task, parameter_set) (or the individual steps: prepare_event_study, fit_model, calculate_statistics)
Extract results with get_ar(), get_car(), get_aar(), or tidy.EventStudyTask
Export with export_results
Visualize with plot_event_study
Generate reports with generate_report

Extensions

cross_sectional_regression – Explain CARs with firm characteristics
IntradayEventStudyTask – Intraday event studies
PanelEventStudyTask – Panel DiD event studies (TWFE, Sun-Abraham, Callaway-Sant'Anna, de Chaisemartin-D'Haultfoeuille, Borusyak-Jaravel-Spiess)
SyntheticControlTask – Synthetic control methods
download_stock_data, download_factor_data – Data helpers