A Major Leap Forward

We are excited to announce EventStudy v0.40.0, a comprehensive overhaul of the R package. This release transforms the package from a focused tool with three return models into a full-featured event study platform with 13 return models, 11 test statistics, and entirely new capabilities including panel event studies, intraday analysis, and publication-ready export.

What’s New

13 Return Models

Beyond the original Market Model, Mean Adjusted, and Market Adjusted models, v0.40.0 adds:

Fama-French 3-Factor Model – size and value factors
Fama-French 5-Factor Model – adds profitability and investment
Carhart 4-Factor Model – adds momentum
GARCH(1,1) – time-varying volatility
BHAR – buy-and-hold abnormal returns for long-horizon studies
Volume Model – abnormal trading volume
Volatility Model – abnormal return volatility
Custom & Linear Factor Models – extensible base classes

11 Test Statistics

The test statistics suite now includes both parametric and non-parametric tests:

AR T-Test, CAR T-Test, BHAR T-Test
Cross-Sectional T-Test
Patell Z Test
BMP Test (Boehmer, Musumeci & Poulsen 1991)
Sign Test and Generalized Sign Test
Rank Test (Corrado 1989)
Calendar-Time Portfolio Test

Panel Event Studies (Difference-in-Differences)

A completely new module for panel event studies, following Miller (2023, JEP):

Static TWFE – two-way fixed effects estimator
Dynamic TWFE – leads and lags for pre-trend testing
Sun & Abraham (2021) – interaction-weighted estimator for staggered treatment
Cluster-robust standard errors via sandwich::vcovCL()

Read the full Panel Event Study guide

Intraday Event Studies

New IntradayEventStudyTask class supports POSIXct timestamps for minute-level or second-level analysis, with a non-parametric significance test based on Rinaudo & Saha (2014).

Read the full Intraday guide

Cross-Sectional Analysis

Regress CARs on firm characteristics with cross_sectional_regression(), featuring HC-robust standard errors via the sandwich package. Also includes car_by_group() for subgroup comparisons and car_quantiles() for distributional analysis.

Diagnostics & Validation

New diagnostic tools ensure your results are robust:

validate_task() – check data quality before analysis
model_diagnostics() – Shapiro-Wilk, Durbin-Watson, Ljung-Box tests
pretrend_test() – test for pre-event abnormal returns
plot_diagnostics() – visual diagnostic charts

Publication-Ready Export

Export your results to multiple formats:

CSV – for further analysis in any tool
Excel – multi-sheet workbook with formatted tables
LaTeX – publication-ready tables with significance stars
Broom – tidy() method for tidyverse workflows

Read the full Diagnostics & Export guide

Quick Example

The new run_event_study() convenience wrapper makes it easy to get started:

library(EventStudy)

# Create task from data
task <- EventStudyTask$new(firm_data, index_data, request)

# Run event study with default settings (Market Model)
ps <- ParameterSet$new()
task <- run_event_study(task, ps)

# View results
task$summary()

Event Study Summary
===================
Events:  2 
Groups:  Tech 
Symbols: AAPL, MSFT 

Model Statistics:
   AAPL : alpha= -0.000756  beta= 0.0549  sigma= 0.018645  R2= 0.0016 
   MSFT : alpha= -0.000261  beta= 0.0311  sigma= 0.020886  R2= 4e-04

plot_event_study(task, type = "caar")

Getting Started

Install the package from GitHub:

# install.packages("devtools")
devtools::install_github("sipemu/eventstudy")

CRAN submission is underway. Check the GitHub repository for the latest updates and full documentation.