What’s New in v0.50.0

Building on the foundation of v0.40.0, this release adds time-varying parameter models, modern DiD estimators, synthetic control methods, and robust inference tools. Here is everything new.

2 New Return Models (15 total)

Rolling Window Model (RollingWindowModel) – time-varying \(\alpha\) and \(\beta\) via rolling OLS. Captures structural breaks without parametric assumptions.
DCC-GARCH Model (DCCGARCHModel) – Dynamic Conditional Correlation GARCH for time-varying beta and volatility. Based on Engle (2002).

# Rolling window with 60-day window
ps <- ParameterSet$new(return_model = RollingWindowModel$new(window_size = 60))

# DCC-GARCH for time-varying correlation
ps <- ParameterSet$new(return_model = DCCGARCHModel$new())

Read the full Expected Return Models guide

Kolari-Pynnönen Test (12 test statistics total)

The new KolariPynnonenTest adjusts the BMP test for cross-sectional correlation caused by event clustering. Essential when multiple events occur on the same calendar date.

ps <- ParameterSet$new(
  multi_event_statistics = MultiEventStatisticsSet$new(
    tests = list(BMPTest$new(), KolariPynnonenTest$new())
  )
)

Read the full Test Statistics guide

3 New Panel DiD Estimators (6 total)

The panel event study module now includes three modern DiD estimators alongside the existing TWFE and Sun & Abraham methods:

Callaway & Sant’Anna (2021) – doubly robust group-time ATTs with flexible aggregation. Requires did.
de Chaisemartin & D’Haultfoeuille (2020) – robust estimation for switchers, handles treatment reversals. Requires DIDmultiplegt.
Borusyak, Jaravel & Spiess (2024) – efficient imputation estimator for staggered adoption. Requires didimputation.

result <- estimate_panel_event_study(
  panel_task,
  method = "callaway_santanna",
  leads = 5, lags = 5
)

Read the full Panel Event Study guide

Synthetic Control Methods

A completely new module for single-unit causal inference. Construct a weighted combination of donor units to approximate the treated unit’s counterfactual, then assess significance via placebo tests.

sc_task <- SyntheticControlTask$new(treated, donors, treatment_time = 25)
result <- estimate_synthetic_control(sc_task)
placebo <- sc_placebo_test(result)
plot_synthetic_control(placebo, type = "placebo")

Read the full Synthetic Control guide

Wild Bootstrap Inference

When asymptotic p-values are unreliable (short estimation windows, non-normal residuals, small samples), the wild bootstrap provides more accurate inference:

boot_result <- bootstrap_test(task, B = 1000, type = "rademacher")

Read the full Inference & Robustness guide

Multiple Testing Corrections

Control false discovery when testing multiple windows, subgroups, or statistics:

adjusted <- adjust_p_values(p_values, method = "BH")  # Benjamini-Hochberg
adjusted <- adjust_p_values(p_values, method = "holm") # Holm (step-down Bonferroni)

Power Analysis via Monte Carlo Simulation

Design your study with confidence. Simulate event studies with known effects to estimate detection power:

sim <- simulate_event_study(
  n_firms = 30,
  n_simulations = 1000,
  abnormal_return = 0.01
)
sim$power  # e.g., 0.82 = 82% detection rate

Read the full Power Analysis guide

Summary

Feature	v0.40.0	v0.50.0
Return models	13	15
Test statistics	11	12
Panel DiD estimators	3	6
Synthetic control	–	New
Wild bootstrap	–	New
Multiple testing corrections	–	New
Power analysis	–	New

Getting Started

Install from GitHub:

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

Check the GitHub repository for the latest updates and full documentation.