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Automated Event Study Reports

Simon Mueller

2026-10-09

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("exec_summary", "data_methods", "diagnostics",
               "results", "robustness", "appendix")
)

Available sections:

Section	Description
`exec_summary`	Study overview: model, event window, number of events
`data_methods`	Data summary, model, and methodology description
`results`	Single- and multi-event AR/CAR/AAR/CAAR results (or panel event-time estimates for a `PanelEventStudyTask`)
`diagnostics`	Model fit diagnostics (residual plots, normality tests)
`robustness`	Robustness notes and the joint-hypothesis caveat
`references`	Knowledge-base references drawn from the diagnostics
`appendix`	Technical details and methodology

Note: exec_summary, data_methods, results, diagnostics, robustness, and references are the six sections included by default; appendix is opt-in.

Summary Only

For a quick overview:

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

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 = ~ size + leverage,
  data = firm_chars
)

generate_report(
  task,
  output_file = "report.html",
  sections = c("exec_summary", "results", "diagnostics"),
  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(
  panel_data = panel_data,
  unit_id = "unit_id",
  time_id = "time_id",
  outcome = "outcome",
  treatment = "treatment",
  treatment_time = "treatment_time"
)
panel_task <- estimate_panel_event_study(panel_task, method = "dynamic_twfe")

generate_report(
  panel_task,
  output_file = "panel_report.html",
  sections = c("exec_summary", "results")
)

estimate_panel_event_study() returns the (mutated) PanelEventStudyTask with results attached, not a separate results object – pass its return value on to generate_report(). Panel-specific content is rendered automatically inside the results section whenever task is a PanelEventStudyTask.

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])
  )
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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