Automated Event Study Reports

Introduction

The EventStudy package can generate comprehensive HTML reports from a completed event study with a single function call. Reports include summary tables, diagnostic plots, test statistics, and optional cross-sectional regression results.

Requirements

Report generation requires the rmarkdown and knitr packages:

install.packages(c("rmarkdown", "knitr"))

Basic Usage

library(EventStudy)

# Run a complete event study
task <- EventStudyTask$new(firm_data, index_data, request)
ps <- ParameterSet$new()
task <- run_event_study(task, ps)

# Generate report
generate_report(task, output_file = "my_report.html")

Customizing the Report

Title and Author

generate_report(
  task,
  output_file = "report.html",
  title = "Earnings Announcement Event Study",
  author = "John Doe"
)

Selecting Sections

Choose which sections to include:

generate_report(
  task,
  output_file = "report.html",
  sections = c("summary", "data", "diagnostics",
               "single_event", "multi_event", "appendix")
)

Available sections:

Section	Description
`summary`	Study overview: model, event window, number of events
`data`	Data summary and event timeline
`diagnostics`	Model fit diagnostics (residual plots, normality tests)
`single_event`	Individual event AR and CAR results
`multi_event`	AAR and CAAR test statistics
`cross_sectional`	Cross-sectional regression results
`panel`	Panel event study results (for PanelEventStudyTask)
`appendix`	Technical details and methodology

Summary Only

For a quick overview:

generate_report(
  task,
  output_file = "summary.html",
  sections = c("summary", "multi_event")
)

Including Cross-Sectional Analysis

If you have firm characteristics, include the cross-sectional regression:

# Run cross-sectional regression first
cs_result <- cross_sectional_regression(
  task,
  formula = car ~ size + leverage,
  characteristics = firm_chars
)

generate_report(
  task,
  output_file = "report.html",
  sections = c("summary", "multi_event", "cross_sectional"),
  cross_sectional = cs_result
)

Output Formats

Currently HTML is the primary format. PDF output requires a LaTeX installation:

generate_report(
  task,
  output_file = "report.pdf",
  format = "pdf"
)

Panel Event Study Reports

The report function automatically detects PanelEventStudyTask and renders panel-specific sections:

panel_task <- PanelEventStudyTask$new(
  data = panel_data,
  unit_col = "unit_id",
  time_col = "time_id",
  treatment_col = "treatment",
  outcome_col = "outcome"
)
result <- estimate_panel_event_study(panel_task)

generate_report(
  panel_task,
  output_file = "panel_report.html",
  sections = c("summary", "panel")
)

Programmatic Report Generation

Generate reports in a loop for multiple configurations:

models <- list(
  MarketModel$new(),
  MarketAdjustedModel$new(),
  FamaFrench3FactorModel$new()
)

for (i in seq_along(models)) {
  ps <- ParameterSet$new(return_model = models[[i]])
  task <- run_event_study(EventStudyTask$new(firm_data, index_data, request), ps)

  generate_report(
    task,
    output_file = sprintf("report_%s.html", class(models[[i]])[1]),
    title = paste("Event Study:", class(models[[i]])[1])
  )
}

Simon Mueller

2026-09-06